Generation of reach, mixture, and pricing utilizing a framework for audience rating estimation

ABSTRACT

Schedule spot counts for allocations of spots are generated based on a selling title and week combination structure for a pending deal for an advertiser, and simulated spot schedules are generated based on schedule spot counts for the advertiser for a quarter. Estimates of unduplicated audience for new spot schedules are generated based on corresponding attributes for each simulated spot schedule and a proposal for the advertiser&#39;s deal is generated based on the unduplicated audience estimates. Half-hour assignments within selling title-weeks are generated based on the schedule spot counts, and are randomly sampled over the quarter to generate the simulated spot schedules. The sampling may be constrained to allow one unit from any simulated spot schedule to air in a half-hour assignment on any given network and selection of half-hour assignments for unit placement are constrained to a specified subset of time, and/or a selling category on networks of interest.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application makes reference to: U.S. application Ser. No.14/842,799, which was filed on Sep. 1, 2015; U.S. application Ser. No.14/842,808, which was filed on Sep. 1, 2015; U.S. application Ser. No.14/842,817, which was filed on Sep. 1, 2015; and U.S. application Ser.No. 14/930,559, which is filed concurrently herewith.

Each of the above referenced patent application and patent is herebyincorporated herein by reference in its entirety.

FIELD OF TECHNOLOGY

Certain embodiments of the disclosure relate to targeting of spots. Morespecifically, certain embodiments of the disclosure relate to a methods,systems, and apparatus for generation of reach, mixture, and pricingutilizing a framework for audience rating estimation.

BACKGROUND

The process of creating proposals for a linear media plan foradvertisers is iterative and time consuming, and is usually limited to asingle network. The process of scheduling linear media advertisementspots into commercial breaks tends to be tedious and prone to errors.While some systems are available to receive an advertiser's order forthe placement of spots into commercial breaks, these products eithersimply try to fill empty holes that exist within already scheduledcommercial breaks on a first come, first placed basis, or do not addressall requirements associated with the commercials. As a result, thesystems currently available typically fail to honor all the constraintsand requirements for each of the spots.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present disclosureas set forth in the remainder of the present application with referenceto the drawings.

BRIEF SUMMARY OF THE DISCLOSURE

Systems and/or methods are provided for generation of reach, mixture,and pricing utilizing a framework for audience rating estimation,substantially as shown in and/or described in connection with at leastone of the figures, as set forth more completely in the claims.

These and other advantages, aspects and novel features of the presentdisclosure, as well as details of an illustrated embodiment thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1A is a block diagram that illustrates an exemplary system forreceiving and processing spot scheduling orders, in accordance with anexemplary embodiment of the disclosure.

FIG. 1B is a diagram of the television advertisement management system,in accordance with an exemplary embodiment of the disclosure.

FIG. 1C is a flow chart illustrating high-level operation of thetelevision advertisement management system of FIG. 1B, in accordancewith an exemplary embodiment of the disclosure.

FIG. 2A is a diagram that illustrates a framework demo audience ratingestimation for a media system, in accordance with various exemplaryembodiments of the disclosure.

FIG. 2B is a diagram that illustrates a focus network determination forthe framework for demo audience rating estimation for the media systemof FIG. 2A, in accordance with various exemplary embodiments of thedisclosure.

FIG. 2C is a diagram that illustrates an exemplary target matrix, inaccordance with various exemplary embodiments of the disclosure.

FIG. 2D is a diagram that illustrates a focus network determination forthe framework for target audience rating estimation based on the targetmatrix of FIG. 2C, in accordance with various exemplary embodiments ofthe disclosure.

FIG. 3 is a high-level diagram illustrating exemplary generation ofaudience rating estimates and a dataset of simulated spot schedules andcorresponding attributes based on demographics and targets across one ormore networks, in accordance with various exemplary embodiments of thedisclosure.

FIG. 4A is a flow chart illustrating exemplary operations for processinga commercial break schedule by an advertisement scheduler, in accordancewith an exemplary embodiment of the disclosure.

FIG. 4B is a flow chart illustrating exemplary operations for providingspot scheduling with targeting, in accordance with an exemplaryembodiment of the disclosure.

FIG. 4C is a flow chart illustrating modification of the lift goal, inaccordance with an exemplary embodiment of the disclosure.

FIG. 4D is a high-level flow chart illustrating exemplary audienceplacement for advertiser spots that have an audience guarantee, inaccordance with an exemplary embodiment of the disclosure.

FIG. 5A is a high-level diagram illustrating exemplary proposalgeneration for a pending deal, in accordance with an exemplaryembodiment of the disclosure.

FIG. 5B is a diagram that illustrates example audience planning for apending deal for an advertiser, in accordance with an exemplaryembodiment of the disclosure.

FIG. 6 is a diagram that illustrates an exemplary allocation ofsimulated spots based on selling title, maximum allocation to sellingtitle, and maximum total units, in accordance with an exemplaryembodiment of the disclosure.

FIG. 7 is a diagram illustrating exemplary operations for a frameworkfor simulated spot schedule generation and sampling, and audience reachestimation, in accordance with an exemplary embodiment of thedisclosure.

FIG. 8 is a diagram that illustrates a distribution framework that maybe utilized to generate unit (spot) distribution across networks andselling title-weeks combinations, in accordance with an exemplaryembodiment of the disclosure.

FIG. 9 is a conceptual diagram illustrating an example of a hardwareimplementation for a television advertisement management systememploying a processing system for audience proposal creation and spotscheduling utilizing a framework for audience rating estimation, inaccordance with an exemplary embodiment of the disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Certain embodiments of the disclosure may be found in a method andsystem for generation of reach, mixture, and pricing utilizing aframework for audience rating estimation. The framework for audiencerating estimation may also be referred to as a framework or model foraudience estimation or model for audience rating estimation.Traditionally, advertisement in television media has been measured bythe gross number of people within a specific demographic (age range plusgender), and does not typically include a reach component. Nowadays,advertisers want to create more focused advertising campaigns thattarget better the customers that will be exposed to them. Theadvertisers may also be interested in how much reach their campaign maybe receiving. The process of creating proposals for a linear media planfor advertisers is iterative and time consuming, is usually limited toindividual networks, and is focused on measuring the overall value tothe advertiser in terms of two main metrics: overall budget, and demoCPM (cost per thousand impressions within the advertiser primary targetdemographic). The combination of these two metrics will yield or give anumber of demo guaranteed impressions that the broadcaster has todeliver. Current systems cannot accommodate distributing units acrossselling titles in multiple networks at the same time, nor can theyoperate to process impressions from more targeted audience segmentswhich are smaller than broader demographics expressed in terms of ageand gender. Additionally, current systems do not typically include areach component. Throughout the disclosure, whenever reference is madeto audience or guaranteed impressions, it should be assumed that suchreference implies gross audience, which is the total views and mayinclude duplicated views.

Various embodiments of the disclosure may comprise a hardwareadvertisement management system that communicates with a plurality ofadvertiser order generation systems and electronically receives, via acommunication network, deals comprising orders from advertisers. Thehardware advertisement management system may generate schedule spotcounts for allocations of spots based on a selling title and weekcombination structure for a pending deal for an advertiser, and aplurality of simulated spot schedules are generated based on theschedule spot counts for the advertiser for a quarter. The schedule spotcounts may be generated based on historical information for possibleallocations of the spots. Estimates of unduplicated audience for newspot schedules may be generated based on corresponding attributes foreach of the plurality of simulated spot schedules and a proposal for thedeal for the advertiser is generated based on the estimates ofunduplicated audience. Half-hour assignments within selling title-weeksmay be generated based on the schedule spot counts, and the half-hourassignments are randomly sampled over the quarter to generate theplurality of simulated spot schedules. For each of the plurality ofsimulated spot schedules, each random schedule resulting from the randomsampling within the half-hour assignments over the quarter providesschedule characteristics, schedule statistics, and a correspondingaudience. The sampling may be constrained to allow one unit from anysimulated spot schedule to air in a half-hour assignment on any givennetwork. The selection of half-hour assignments for unit placement maybe constrained to a specified subset of time, and/or a selling categoryon networks of interest. Simulated spot statistics may be computed orgenerated for each of the plurality of simulated spot schedules based onthe corresponding attributes. The simulated spot statistics comprises atleast one of unduplicated viewers and panel weights, spot schedule grossrating points for target or demographic audience, spot schedule unitcount, spot schedule length (in days), spot schedule separation, day ofweek distribution, time of day distribution, and network and sellingtitle distribution. In accordance with another aspect of the disclosure,the advertisement management system may determine a target cost perthousand (CPM) baseline and a demographics CPM baseline for the pendingdeal for the advertiser in which audience spots will be offered, and mayestablish parameters for the pending deal for the advertiser.Constraints to be imposed on the pending deal may be determined based ona target CPM reduction goal, a demographics CPM cap, and establishedparameters for the pending deal, and rates may be generated by sellingtitle for each selling title of a plurality of selling titles, for eachweek of a plurality of weeks for a duration of the pending deal, and foreach network of a plurality of networks, for the pending deal for theadvertiser, based on the constraints. Concurrent with the establishingof the parameters, the determining of the constraints, and thegenerating of the rates by selling title, target audience ratingestimates may be acquired based on the target CPM reduction goal, andthe demographics CPM cap for the plurality of networks. A distributionof the audience spots is generated across the plurality of sellingtitles, the plurality of weeks, and the plurality of networks based atleast in part on the target audience rating estimates, reach estimates,a budget for the pending deal, the generated rates by selling title, andavailable inventory per selling title-weeks, and the proposal may begenerated based on the distribution. Audience spots may be scheduledacross one or more networks for selling title-weeks based on thegenerated distribution. The parameters for the pending deal may comprisethe budget, the target CPM reduction goal, the demographics CPM cap, anddemographics rates per selling title to be charged per spot for thepending deal for the advertiser. The distribution of the audience spotsmay be generated utilizing a distribution framework, and inputs to thedistribution framework comprise the budget, the target CPM reductiongoal, the demographics CPM cap, the estimates of unduplicated audience,estimates of gross audience, and one or more of a maximum number ofunits to be allocated per selling title-week, a limit on the totalnumber of units in the pending deal, a limit on the number ofimpressions and/or units percentages by network or selling title, alimit on rate increase, and an indication of whether to increase therates in the same proportion.

FIG. 1A is a block diagram that illustrates an exemplary system forreceiving and processing spot scheduling orders, in accordance with anexemplary embodiment of the disclosure. Referring to FIG. 1A, there isshown a television advertisement scheduling system 100 that iscommunicatively coupled to advertiser order generation systems 130 a, .. . , 130 n through a network 120. Consumer devices 132 a, . . . , 132 nare communicatively coupled to the network 120. The televisionadvertisement scheduling system 100 may comprise a spot scheduler solver110, an advertisement scheduler 112, a targeting processor 113, and anaudience processor 114.

The network 120 may be any kind of network, or a combination of variousnetworks, and it is shown illustrating the communication that may occurbetween the advertiser order generation systems 130 a, . . . , 130 n andthe television advertisement scheduling system 100. For example, thenetwork 120 may comprise one or more of a cable television network, theInternet, a satellite communication network, a wide area network (WAN),a medium area network (MAN), and a local area network (LAN).

The television advertisement scheduling system 100 may be operated by abroadcasting company and may comprise a spot scheduler solver 110. Thebroadcast company may be operable to multicast content via a pluralityof channels, for example, traditional over-the-air broadcast channels,cable television networks, satellite communication networks, theInternet, and/or other content delivery networks. The spot schedulersolver 110 may comprise suitable logic, circuitry, code, and/orinterfaces that may be operable to receive orders from one or more ofthe advertiser order generation systems 130 a, . . . , 130 n to placeadvertisements spots into one or more commercial breaks that occurduring television programming broadcast. That is, the spot schedulersolver 110 may be utilized to determine the appropriate scheduling ofadvertisement spots to produce a commercial break schedule according tothe information provided by the advertiser order generation systems 130a, . . . , 130 n. The advertiser order generation systems 130 a, . . . ,130 n may place orders with the broadcasting company that includeinformation about the type of spots to be broadcast, the number of spotsto be broadcast, and when should the spots be aired. In this regard, theadvertiser order generation systems 130 a, . . . , 130 n mayelectronically book spots to a selling title (ST), and within theselling title there are constraints. The advertiser order generationsystems 130 a, . . . , 130 n may provide multiple orders, which need notbe submitted at the same time. Therefore, the spot scheduler solver 110may continuously receive orders with new or additional spots to beplaced and may need to update any previously determined commercial breakschedule to accommodate the constraints and requirements of those spotsalready placed and of the new spots being received.

The television advertisement scheduling system 100 may have a cutoffafter which orders submitted by the advertiser order generation systems130 a, . . . , 130 n may not be considered by the spot scheduler solver110 when preparing a next commercial break schedule. A commercial breakschedule may include a final or complete schedule of the spots that areto appear during a specified period of television programming such ashours, days, weeks, or a combination thereof.

Various embodiments of the disclosure, the television advertisementscheduling system 100, may comprise a spot scheduler solver 110 and anadvertisement scheduler 112. Although the spot scheduler solver 110 andthe advertisement scheduler 112 are illustrated as separate entities,they may be integrated as a single entity in which the advertisementscheduler 112 may be enabled or disabled utilizing, for example, one ormore parameters. The television advertisement scheduling system 100 maybe operable to electronically receive, via the communication network120, deals comprising advertisers orders from the plurality ofadvertiser order generation systems 130 a, . . . , 130 n. The spotscheduler solver 110 may be operable to receive an advertiser's order toplace one or more spots into one or more commercial breaks. Theadvertiser's order comprises airing constraints and placementrequirements corresponding to the one or more spots, and each of the oneor more commercial breaks comprises a plurality of inventory buckets.The airing constraints corresponding to each of the one or more spotscomprise one or more of network constraints, selling title constraints,inventory type constraints, allowable date and time constraints, andadvertiser conflict constraints. The placement requirementscorresponding to each of the one or more spots comprise one or more ofassociative constraints, position constraints, time separationconstraints, franchise and/or title exclusion constraints, and spotpinning constraints. The associative constraints define the positioningof any two or more spots relative to each other within the same one ofthe at least one of the plurality of inventory buckets or in adjacentinventory buckets. The position constraints define the positioning ofany one spot in one of the at least one of the plurality of inventorybuckets and/or in a commercial break.

A position of each of the plurality of inventory buckets within each ofthe one or more commercial breaks may define a sequencing order of eachof the inventory buckets within each of the one or more commercialbreaks, and each of the plurality of inventory buckets comprise acorresponding inventory type that indicates a type of content in each ofthe plurality of inventory buckets. The spot scheduler solver 110 may beoperable to assign each of the one or more spots to at least one of theplurality of inventory buckets that are within each of the one or morecommercial breaks based on the airing constraints and placementrequirements. The spot scheduler solver 110 may be operable to match thecharacteristics of the assigned at least one of the plurality ofinventory buckets that are within each of the one or more commercialbreaks with the airing constraints and requirements of each of the oneor more spots. The spot scheduler solver 110 may be operable to rank theone or more spots that are within each of the assigned at least one ofthe plurality of inventory buckets that are within each of the one ormore commercial breaks based on the matching such that the airingconstraints and placement requirements corresponding to the one or morespots are fulfilled. The spot scheduler solver 110 may be operable toreshuffle the one or more spots that are within each of the assigned atleast one of the plurality of inventory buckets that are within each ofthe one or more commercial breaks. When at least one of the one or morespots are not assigned to the at least one of the plurality of inventorybuckets that are within each of the one or more commercial breaksbecause of conflicts, the spot scheduler solver 110 may be operable toperform a prioritization scheme to complete the assignment of the one ormore spots that are not assigned. The spot scheduler solver 110 may beoperable to prioritize the spots based on arrival lead time such thatall spots for a particular order are given same priority, and prioritiesare chosen such that a sum of all priorities post a certain arrival timeis less than a priority on any spot prior to the certain arrival time.The spot scheduler solver 110 may also be operable to maximize the sumof the rates corresponding to the one or more spots. The spot schedulersolver 110 may also be operable to maximize the spread of days in whichthe one or more spots occur over the duration of the commercial breakschedule. The spot scheduler solver 110 may be operable to generate acommercial break schedule based on the completed assignment of the oneor more spots that are not assigned. It should readily be understood byone of skill in the art that other metrics may be utilized withoutdeparting from the spirit and scope of the various embodiments of thedisclosure.

The spot scheduler solver 110 may also be operable to receive anadditional advertiser's order to place one or more additional spots intothe one or more commercial breaks, wherein the additional advertiser'sorder comprises constraints and placement requirements corresponding tothe one or more additional spots. The spot scheduler solver 110 may alsobe operable to assign each of the one or more spots and the one or moreadditional spots to at least one of the plurality of inventory bucketswithin the one or more commercial breaks based on the correspondingconstraints and placement requirements. When at least one of the one ormore spots and the one or more additional spots is not assigned becauseof conflicts, the spot scheduler solver 110 may also be operable toperform a prioritization scheme to complete the assignment of the atleast one spot that is not assigned. The spot scheduler solver 110 mayalso be operable to modify the generated commercial break schedule basedon the completed assignment of the at least one spot that is notassigned. The spot scheduler solver 110 may be operable to communicatethe generated commercial break schedule to the advertisement scheduler112 for processing.

The advertisement scheduler 112 may be operable to receive the generatedcommercial break schedule, and determine a current indexingrepresentative of the liability per pending spot for one or more of thedeals which are guaranteed on demographic impressions. The advertisementscheduler 112 may be operable to reshuffle and prioritize the placementof the one or more spots based on one or more metrics such as a currentindexing of the one or more deals in order to minimize liabilityassociated with the one or more deals. The current indexing representsthe projected liability per pending spot of a given deal and isdetermined based on what has already been aired, and what will be airedin the future. The reshuffling may be based on a forecast of expecteddemo viewership associated with the one or more deals. The advertisementscheduler 112 may be operable to generate a finalized commercial breakschedule based on the reshuffling and apply the finalized schedule tothe log. It should readily be understood by one of skill in the art thatthe other metrics other than the current indexing, which represents theprojected liability per pending spot, may be utilized without departingfrom the spirit and scope of the various embodiments of the disclosure.

In accordance with various embodiments of the disclosure, anadvertisement management system 100, which comprises a spot schedulersolver 110 and an advertisement scheduler 112, is operable tocommunicate with a plurality of advertiser order generation systems 130a, . . . , 130 n, and electronically receives, via a communicationnetwork 120, deals comprising advertiser orders. The advertisementscheduler 112 receives a commercial break schedule generated from acompleted assignment of one or more spots, wherein the one or more spotscorrespond to the deals comprising the advertiser orders to place theone or more spots into one or more commercial breaks in the commercialbreak schedule based on constraints and placement requirements. Theadvertisement scheduler 112 may determine a current indexing for one ormore of the deals which are guaranteed on demographic impressions, andreshuffles the placement of the one or more spots based on the currentindexing of the one or more deals in order to minimize liabilityassociated with the one or more deals. The reshuffling is based on aforecast of expected viewership associated with the one or more deals.The reshuffling of the placement of the one or more spots is done acrossone or more channels airing the one or more spots. The advertisementscheduler 112 may generate a finalized commercial break schedule basedon the reshuffling. The expected viewership may be forecasted for aparticular period based in the status of prior logs. As an example, andwithout limitation, the expected viewership may be forecasted based onthe status of the prior logs for an upcoming 7-day period.

The advertisement scheduler 112 may update a current forecast ofexpected demo viewership at the end of the particular period based onactual ratings for a prior week, which may be derived at last in partfrom Nielsen ratings and/or other data sources. The advertisementscheduler 112 may maximize delivery for candidate spots that areunder-delivering (that is, underperforming deals or deals that are notpacing as expected, where pacing is a computation of cumulativedemographics guaranteed so far, that is, at a particular point in time),and reshuffle the placement of the one or more spots such that thecandidate spots get maximum delivery in order to minimize the liability.For example, the advertisement scheduler 112 may determine or identifycandidate spots with liability above a certain value such as a thresholdvalue, which indicates that the spots are not pacing as expected orunder-delivering, and may reshuffle the placement of the one or morespots such that the candidate spots achieve maximum delivery, therebyminimizing liability. The candidate spots may be determined oridentified based on their current delivery value. The advertisementscheduler 112 may determine each demographic for each of the one or morespots and generate, for each demographic, an estimate of the expectedviewership associated with the one or more spots.

The advertisement scheduler 112 may generate the estimate of theexpected viewership for specified time intervals. The time intervals maybe, for example, 30-minute time intervals. The advertisement scheduler112 may determine a current indexing for each of a plurality of thedeals which are guaranteed on demographic impressions, and providepreferential treatment to placement of the one or more spots for one ormore of the plurality of deals having greatest liability based on thedetermined current indexing when performing the reshuffling. Theadvertisement scheduler 112 may modify the forecast of expected demoviewership associated with the one or more deals based on a weightingfactor, which may be updatable. The advertisement scheduler 112 mayupdate the weighting factor over time to improve the current indexing ofthe one or more deals over time.

The targeting processor 113 may comprise suitable logic, circuitry, andinterfaces that may be operable to execute code that may be operable tohandle the processing of one or more orders in one or more deals thatare targeting orders. The targeting processor 113 may be operable toacquire and/or determine target audience rating estimates for targetedspots, and may handle the placement of the targeted spots based on theaudience rating estimates. The targeting processor 113 may also beoperable to determine a lift goal, determine whether the lift goal maybe achieved, and modify the lift goal in instances where the lift goalcannot be achieved.

The audience processor 114 may comprise suitable logic, circuitry, andinterfaces that may be operable to execute code to schedule audiencespots based on a distribution of audience spots that is generatedutilizing a distribution framework. The distribution framework isutilized to generate a distribution of audience spots across a pluralityof networks, across a plurality of selling titles, and across aplurality of weeks for the duration of a deal for an advertiser based onvarious inputs. The units for a particular advertiser order may then bebooked based on the distribution for the selling title and weekscombinations across one or more the networks. The selling title andweeks combinations may also be referred to as selling title-weekscombinations, or selling title-weeks. While the targeting processor 113is operable to target a single network, the audience processor 114 isoperable to schedule audience spots across one or more networks forselling title-week combinations.

In accordance with an aspect of the disclosure, each of theadvertisement scheduler 112, the targeting processor 113, or theaudience processor 114 may operate independently. In another aspect ofthe disclosure, any two of the advertisement scheduler 112, thetargeting processor 113, and/or the audience processor 114 may operateconcurrently. In other aspects of the disclosure, the advertisementscheduler 112, the targeting processor 113, and the audience processor114 may also operate concurrently, or may not operate at all. In thisregard, in instances when the advertisement scheduler 112, the targetingprocessor 113, and the audience processor 114 do not operate, then onlythe spot scheduler solver 110 operates.

Reach may generally be defined as the cumulative number of unique,unduplicated, or original impressions. These impressions may be eitherdemographic impressions or target impressions. Consider the followingtable for an advertisement campaign with three (3) spots.

TABLE 1 Spot Schedule Duplicated Reach Cumulative Reach Audience Ad.Schedule (000) (000) (000) Spot 1 200 200 0 Spot 2 300 400 100 Spot 3250 500 150 Gross Audience (000) 750 Schedule Cumulative Reach 500Schedule Frequency 1.5Referring to Table 1, spot 1 received a total of 200 k views orimpressions. The schedule cumulative reach at the end of airing spot 1is 200 k, and the duplicated audience is 0, since this was the firsttime spot 1 was aired and all views or impressions are unique. Spot 2received a total of 300 impressions, of which 100 were duplicatedaudience (i.e. 200 unduplicated audience). Accordingly, at the end ofairing spot 2, the schedule cumulative reach is 400 (200+200). Spot 3received a total of 250, of which 150 were duplicated audience (i.e. 100unduplicated audience). Accordingly, at the end of airing spot 3, theschedule cumulative reach is 500 (200+200+100). Hence, the grossaudience (GA) is 750 (200+300+250), and the schedule frequency, whichrepresents the average number of units viewed by qualified audience is1.5.

In the field of television viewership and advertisement, reach may bemeasured based on various qualifications, namely (1) viewing, and (2)frequency. An example of a viewing qualification is duration, whichprovides an indication of how long the spot was viewed. Accordingly,viewing may be qualified on exposure to a spot. For example, theduration may indicate a viewer watched the spot for at least, forexample, 1 minute. It should be readily understood that durations thatare, for example, less than 1 minute, or greater than 1 minute, may beutilized without departing from the spirit and scope of the variousembodiments of the disclosure. An example of a frequency qualificationmay indicate that the viewer viewed at least 2 spots.

In accordance with an embodiment of the disclosure, a network providermay negotiate a deal with an advertiser to guarantee the advertiser aspecified reach, which is measured by impressions or views based on thecorresponding qualifications. For example, the network provider mayguarantee a reach of 1 million impressions to the advertiser for thedeal. In order to determine the allocation of spots that will providethe guaranteed reach, the different possible combinations of allocationof spots for each selling title and weeks combinations may be generated.A proposed allocation of spots may be utilized to generate simulatedschedules from which a dataset of simulated spot schedules andcorresponding attributes (simulated spot schedule attributes) may begenerated. A prediction model or reach prediction framework may then beutilized to generate audience reach for new spot schedules based on thedataset. In this regard, the reach prediction framework is fit based onthe simulated spot schedules.

FIG. 1B is a diagram of the television advertisement management system,in accordance with an exemplary embodiment of the disclosure. Referringto FIG. 1B, there is shown a television advertisement management system140, which may be similar to the television advertisement managementsystem 100 of FIG. 1A. The television advertisement management system140 comprises proposal system 142, inventory system 144, traffic system146, placement system 148, and advertisement scheduler 150, targetingprocessor 151, audience processor 153, and LOG/BINS 152. The proposalsystem 142, and the inventory system 144 may be referred to as an orderbooking system 154. The traffic system 146, the advertisement scheduler150, the targeting processor 151, and the audience processor 153 may bereferred to as a spot scheduler 156.

The proposal system 142 comprises hardware systems that are operable tocreate and negotiate proposals with the advertiser order generationsystems 130 a, . . . , 130 n, which are illustrated in FIG. 1A, toproduce one or more deals. A negotiated deal may have one or moreorders. A demographics only deal may be specific to a single network.However, deals that have an audience component may span a plurality ofnetworks. The proposal system 142 comprises a demographics (demo)proposal creator 142 a, a proposal converter 142 b, and an audienceproposal creator 142 c.

The demographics proposal creator 142 a may comprise suitable logic,processor and/or circuitry, and interfaces that may enable execution ofcode to create and negotiate the demographics proposals.

The audience proposal creator 142 c may comprise suitable logic,processor and/or circuitry, and interfaces that may be operable toexecute code that may be operable to create audience proposals acrossone or more networks. The audience proposal creator 142 c may also bereferred to as a hardware audience proposal creator or a hardwareaudience proposal creator processor. The audience proposal creator 142 cmay be operable to utilize a distribution framework to generate adistribution of audience spots across selling title-weeks across aplurality of the networks in order to create the audience proposals. Theaudience proposal creator 142 c utilizes the distribution framework togenerate a distribution of audience spots across a plurality ofnetworks, across a plurality of selling titles, and across a pluralityof weeks for the duration of a deal for an advertiser based on variousinputs. The inputs to the distribution framework may comprise, forexample, for each network, and for each selling title and weekcombination, there is a corresponding rate parameter, demographics(demo) impressions or audience for each demo parameter, targetimpression or audience for each target parameter, and an availsparameter, which represents the air time that is available to allocatespots. The inputs to the distribution framework are described in moredetail with respect to, for example, FIG. 8, and its correspondingdescription.

The audience proposal creator 142 c may also be operable to handleproposals for deals that may have a reach component such as reach deals,or mixture deals that have an audience component and a reach component.In this regard, the audience proposal creator 142 c may be operable togenerate a dataset of simulated schedules and corresponding attributes.The audience proposal creator 142 c may utilize a prediction model togenerate audience reach for new spot schedules based on the dataset ofsimulated spot schedules and corresponding attributes. The generatedaudience reach information may be incorporated in proposals andcommunicated to the proposal converter 142 b.

The proposal converter 142 b may comprise suitable logic, processorand/or circuitry, and interfaces that is operable to facilitateexecution of code to accept and convert the negotiated proposals to oneor more orders. In this regard, the proposal converter 142 b is operableto receive and accept negotiated proposals from the demographicsproposal creator 142 a, and the audience proposal creator 142 c andconvert the negotiated proposals to one or more orders.

In accordance with an embodiment of the disclosure, in operation, theaudience proposal creator 142 c may produce a distribution of audiencespots utilizing the distribution framework. The distribution of audiencespots shows the allocation of audience spots across a plurality ofselling titles, across a plurality of weeks corresponding to theduration of a deal, and across a plurality of networks. The audienceproposal creator 142 c may generate an audience proposal for a pendingdeal for an advertiser. Accordingly, audience spots may be booked basedon the distribution of the audience spots across the selling-title weeksand across the plurality of networks.

The inventory system 144 comprises inventory clearance system 144 a, andorder modification system 144 b. The inventory clearance system 144 a isoperable to clear the orders based on available inventory. The ordersmay be cleared, for example, on a first come first serve (FCFS) basis.When an order is received, the order may specify the x units be placedin selling title week A, y units be placed in selling title week B, andz units be placed in selling title week C, for example. Since there maybe multiple bookings occurring, the inventory clearance system 144 aensures the availability of the inventory for booking the required unitsfor the order. The order modification system 144 b may be operable tochange the attributes associated with an order that has already clearedinventory, add one or more units for an order to the inventory, ordelete one or more units from inventory.

The traffic system 146 may comprise suitable hardware that may beoperable to receive cleared and/or modified orders and queue them forassignment to an available spot scheduler solver. In this regard, thetraffic system 146 a may comprise a hardware queue 146 a, and aplurality of spot scheduler solvers 146 b. The queue 146 a may beoperable to queue cleared and/or modified orders by the traffic system146. The traffic system 146 may assign the cleared and/or modifiedorders that are queued in the queue 146 a to an available spotsscheduler solver in the pool of spot scheduler solvers 146 b for spotscheduling. The placement system 148 is operable to place the spots onthe logs/bins 152 based on the results from the spot scheduler solvers.

The advertisement scheduler 150 may be operable to receive the generatedcommercial break schedule, determine a current indexing representativeof the liability per pending spot for one or more of the deals which areguaranteed on demographic impressions, reshuffle and prioritize theplacement of the one or more spots based on one or more metrics such asa current indexing of the one or more deals in order to minimizeliability associated with the one or more deals. The reshuffling may bebased on a forecast of expected demo viewership associated with the oneor more deals. The advertisement scheduler 150 generates a finalizedcommercial break schedule based on the reshuffling and applies thefinalized schedule to the log.

The targeting processor 151 may handle the processing of targetingorders. In this regard, the targeting processor 151 may be operable toacquire and/or determine target audience rating estimates for targetedspots, and may handle the placement of the targeted spots based on thetarget audience rating estimates. The targeting processor 151 may alsobe operable to determine a lift goal, determine whether the lift goal isachievable, and modify the lift goal in instances where the lift goal isnot achievable.

The audience processor 153 may handle the audience scheduling operationsrelated to deals. For example, the audience processor 153 may beoperable to schedule audience spots based on the distribution ofaudience spots that is generated or produced by the audience proposalcreator 142 c based on a distribution framework. The distributionframework is utilized to generate a distribution of audience spotsacross a plurality of networks, across a plurality of selling titles,and across a plurality of weeks for the duration of a deal for anadvertiser based on various inputs. The audience processor 153 mayschedule the units for a particular advertiser order based on thedistribution for the selling title-weeks combinations across one or morenetworks

In accordance with an exemplary embodiment of the disclosure, thehardware advertisement management system 100 communicates with aplurality of advertiser order generation systems 130 a, . . . , 130 n,and electronically receives, via a communication network, dealscomprising orders from advertisers. The audience proposal creator 142 cmay determine a target CPM baseline and demo CPM baseline for a pendingdeal for an advertiser in which audience spots will be offered. Theaudience proposal creator 142 c may establish parameters for the pendingdeal for the advertiser, determine constraints to be imposed on thepending deal based on a target CPM reduction goal, a demographics CPMcap, and the established parameters, and generate rates by selling titlefor each selling title of a plurality of selling titles, for each weekof a plurality of weeks for a duration of pending deal, and for eachnetwork of a plurality of networks, for the pending deal for theadvertiser, based on the constraints. Concurrent with the establishingof the parameters, the determining of the constraints, and thegenerating of the rates by selling title, the audience proposal creator142 c may acquire target audience rating estimates based on the targetand demo applicable to the advertiser for the plurality of networks. Theaudience proposal creator 142 c may generate a distribution of theaudience spots across the plurality of selling titles, the plurality ofweeks, and the plurality of networks based at least in part on thetarget audience rating estimates, a budget for the pending deal, thegenerated rates by selling title, and available inventory per sellingtitle and weeks combination. The generated distribution satisfies thedetermined constraints as part of the process of generating thedistribution. The audience proposal creator 142 c may generate aproposal based on the distribution. The audience processor 153 or 114may schedule audience spots across one or more networks for sellingtitle and weeks combinations based on the generated distribution.

The generation of the distribution of the audience spots by the audienceproposal creator 142 c includes generating a new distribution of theaudience spots across the plurality of selling titles, the plurality ofweeks, and the plurality of networks based on input from the advertiser,and generating a new proposal based on the new distribution. Theaudience proposal creator 142 c may designate the new proposal as afinal proposal. The audience processor 153 or 114 may schedule theaudience spots across one or more networks for selling title and weekscombinations based on the new distribution.

Exemplary parameters for the pending deal may include the budget, thetarget CPM reduction goal, the demographics CPM cap, and demographicsrates per selling title to be charged per spot for the pending deal forthe advertiser. The audience proposal creator 142 c may be operable togenerate the distribution of the audience spots utilizing a distributionframework. Exemplary inputs to the distribution framework may includethe budget, the target CPM reduction goal, and the demographics CPM cap.Other exemplary inputs to the distribution framework may include one ormore of a maximum number of units to be allocated per selling title andweek combination, a limit on the total number of units in the pendingdeal, a limit on the number of impressions and/or units percentages bynetwork or selling title, a limit on rate increase, and an indication ofwhether to increase the rates in the same proportion. The targetimpressions corresponding to the distribution of the audience spots areguaranteed.

Orders requiring advertisement scheduling may be processed by theadvertisement scheduler 150 and placed on the logs/bins 152 based on theresults from the advertisement scheduler 150. The advertisementscheduler 150, the targeting processor 151, and/or the audienceprocessor 153 may operate independently, or concurrently. For example:(1) any one of the advertisement scheduler 150, the targeting processor151, and/or the audience processor 153 may operate independently; (2)any two of the advertisement scheduler 150, the targeting processor 151,and/or the audience processor 153 may operate concurrently; (3) theadvertisement scheduler 150, the targeting processor 151, and theaudience processor 153 may all operate concurrently; or (4) neither ofthe advertisement scheduler 150, the targeting processor 151, and theaudience processor 153 may operate, and in this case, only the spotscheduler solver 110 operates.

FIG. 1C is a flow chart illustrating high-level operation of thetelevision advertisement management system of FIG. 1B, in accordancewith an exemplary embodiment of the disclosure. Referring to FIG. 1C,there are shown exemplary operations 161 through 167. At 161, the demoproposal creator 142 a and/or the audience proposal creator 142 c createproposals, which may incorporate reach. At 162, the proposal converter142 b receives and converts the proposals to one or more orders. At 163,the inventory clearance system 144 a receives the orders and clears theinventory. At 164, if orders require modification, the ordermodification system 144 b modifies the orders. At 165, the trafficsystem 146 receives cleared and/or modified orders with spots that havecleared inventory and queues them in the queue 146 a for spotscheduling. At 166, the traffic system 146 determines availability ofspot scheduling solvers from a pool of spot scheduling solvers 146 b,assigns one or more spot scheduling solvers to process the spots, andhandles the scheduling and the targeting of orders. At 167, theplacement system 148 places the spots on the log/bins 152 based on theresults from the spot scheduler solvers.

FIG. 2A is a diagram that illustrates a framework for demo audiencerating estimation for a media system, in accordance with variousexemplary embodiments of the disclosure. Referring to FIG. 2A, there isshown an exemplary base matrix 200 comprising a plurality of columns anda plurality of rows. Each of the plurality of columns represents aparticular network among all networks, and each of the rows representsthe 48 30-minute periods/segments in a day (24-hour period). Asillustrated, there are shown exemplary networks, TBS, TNT, TruTV, CNN,ABC, CBS, NBC, . . . , FOX, USA, FX, and A&E.

Each element in the base matrix 200 may be represented by a demo matrix,which comprises a plurality of demo matrix elements, representing alldemographics. For example, demo matrix 210 represents all thedemographics for TBS network, for the 0-30 (1^(st)) 30-minute period.The demo matrix 210 comprises demo matrix elements 210 a, 210 b, 210 c,. . . , 210(N−1), 210N, where N is an integer greater than or equalto 1. Demo matrix element 210 a comprises a first demo D1 for TBS, whichincludes all program attributes (PA), and time attributes (TA), and iscollectively represented as D1_TBS[PA,TA]. Demo matrix element 210 bcomprises a second demo D2 for TBS, which includes all programattributes (PA), and time attributes (TA), and is collectivelyrepresented as D2_TBS[PA,TA]. Demo matrix element 210 c comprises athird demo D3 for TBS, which includes all program attributes (PA), andtime attributes (TA), and is collectively represented as D3_TBS[PA,TA] .. . . Demo matrix element 210(N−1) comprises a (N−1)^(th) demo D(N−1)for TBS, which includes all program attributes (PA), and time attributes(TA), and is collectively represented as DN−1_TBS[PA,TA]. Demo matrixelement 210(N) comprises a N^(th) demo D(N) for TBS, which includes allprogram attributes (PA), and time attributes (TA), and is collectivelyrepresented as DN−1_TBS[PA,TA].

The demo matrix 212 represents all the demographics for TBS network, forthe 1350-1380 (46^(th)) 30-minute period. The demo matrix 212 comprisesdemo matrix elements 212 a, 212 b, 212 c, . . . , 212(N−1), 212N, whereN is an integer greater than or equal to 1. Demo matrix element 212 acomprises a first demo D1 for TBS, which includes all program attributes(PA), and time attributes (TA), and is collectively represented asD1_TBS[PA,TA]. Demo matrix element 212 b comprises a second demo D2 forTBS, which includes all program attributes (PA), and time attributes(TA), and is collectively represented as D2_TBS[PA,TA]. Demo matrixelement 212 c comprises a third demo D3 for TBS, which includes allprogram attributes (PA), and time attributes (TA), and is collectivelyrepresented as D3_TBS[PA,TA] . . . . Demo matrix element 212(N−1)comprises a (N−1)^(th) demo D(N−1) for TBS, which includes all programattributes (PA), and time attributes (TA), and is collectivelyrepresented as DN−1_TBS[PA,TA]. Demo matrix element 212(N) comprises aN^(th) demo D(N) for TBS, which includes all program attributes (PA),and time attributes (TA), and is collectively represented asDN−1_TBS[PA,TA].

The demo matrix 214 represents all the demographics for USA network, forthe 180-210 (7^(th)) 30-minute period. The demo matrix 214 comprisesdemo matrix elements 214 a, 214 b, 214 c, . . . , 214(N−1), 214N, whereN is an integer greater than or equal to 1. Demo matrix element 214 acomprises a first demo D1 for USA, which includes all program attributes(PA), and time attributes (TA), and is collectively represented asD1_USA[PA,TA]. Demo matrix element 214 b comprises a second demo D2 forUSA, which includes all program attributes (PA), and time attributes(TA), and is collectively represented as D2_USA[PA,TA]. Demo matrixelement 214 c comprises a third demo D3 for USA, which includes allprogram attributes (PA), and time attributes (TA), and is collectivelyrepresented as D3_USA[PA,TA] . . . . Demo matrix element 214(N−1)comprises a (N−1)^(th) demo D(N−1) for USA, which includes all programattributes (PA), and time attributes (TA), and is collectivelyrepresented as DN−1_USA[PA,TA]. Demo matrix element 214(N) comprises aN^(th) demo D(N) for USA, which includes all program attributes (PA),and time attributes (TA), and is collectively represented asDN−1_USA[PA,TA].

Each demo matrix element for each network includes program attributes,and time attributes, which may be represented as a matrix [PA,TA].Exemplary time attributes may comprise the following:

-   -   Quarter, Year (if necessary), which may be represented as        variables;    -   Daily seasonality, which may be represented as a linear        combination of trigonometric functions;    -   Day of week (DOW), which may be represent as variables;    -   Half hour, which may be represented as variables, for example,        8:00-8:30, 8:30-9:00;    -   Holidays and special events, for example, Super Bowl, Labor Day        Sunday and Monday, 4^(th) of July (Independence Day), Christmas        Day, and New Year's Eve, and may be represented as variable.

Exemplary program attributes may comprise the following:

-   -   Genre by network/competitor:        -   a. “light content” (soap, comedy, variety, game, music,            reality);        -   b. “heavy content” (documentary, drama, current affairs,            news, science, travel);        -   c. Sports;        -   d. Movies; and        -   e. News;        -   for each type of competitor (bucket), for example, Broadcast            light content, Non-Owned Cable light content;    -   Repeat/Premiere/Live (from Nielsen)    -   Duration (in min)    -   Same program indicator—half hour (hhr) is within same program as        previous (calculated)    -   Lead in genre match—half hour (hhr) starts a program similar        with previous hhr's program (calculated), where genre is light,        heavy, sports or movies        It should be recognized by those skilled in the art that other        time attributes and/or program attributes may be utilized        without departing from the spirit and/or scope of the various        embodiments of the disclosure.

For a given 30-minute period, the probability of a consumer choosing towatch what is on a particular channel is a function of what is currentlyairing on that particular channel, and what the competitors are airingwithin the same 30-minute period. For example, the probability for aconsumer watching TBS network for a given 30-minute period, P_(hhr,TBS)may be represented by the following expression:

$\begin{matrix}{P_{{hhr},{TBS}} = \frac{e^{\beta_{TBS} \cdot x_{p_{{hhr},{TBS}}}}}{\sum\limits_{i = {TBS}}^{Others}e^{\beta_{i} \cdot x_{p_{{hhr},i}}}}} & \left( {{equation}\mspace{14mu} 1} \right)\end{matrix}$where:x=program attributes and time attributes;β=vector of weights to be estimated for a particular network e.g.β_(TBS)=vector of weights to be estimated for TBS.The program attributes and time attributes may be referred to ascovariates, and may be received from one or more entities and/or storagedevices that provide media analytical information, for example, Nielsen.

The P_(hhr,TBS) for equation 1 may be simplified and represented as:ln(Demo(000)_(hhr,TBS))=β_(TBS) ·x _(p) _(hhr,TBS)   (equation 2)

In accordance with an exemplary embodiment of the disclosure, equation 2may be estimated through minimizing squared errors (MSE) utilizingnon-linear optimization.

In accordance with various embodiments of the disclosure, the competitornetworks may be bucketized, i.e. placed into buckets or bins. Thefollowing illustrates exemplary competitor buckets:

-   -   Owned competitors—for example, TBS, TNT, ADSM, TruTV, etc are        owned by the same entity, and thus compete against each other;    -   Broadcast competitors—ABC, CBS, NBC, FOX, CW, ION;    -   Non-Owned cable competitors—USA, Spike, FX, A&E, Lifetime, CMDY;        and    -   All other networks.

The base matrix 200 as illustrated contains data for a single day. Thestructure of the base matrix 200 may be replicated 365 times andpopulated with the corresponding program attributes and time attributesfor a single year. This resulting structure may be replicated andpopulated with the corresponding program attributes and time attributesto provide a corresponding plurality of years of data.

FIG. 2B is a diagram that illustrates a focus determination for theframework for demo audience rating estimation for a media system of FIG.2A, in accordance with various exemplary embodiments of the disclosure.Referring to FIG. 2B, there is shown an exemplary focus matrix 230including a focus network, TBS, and the bucketized networks in buckets,B1, B2, B3, and B4. The focus matrix 230 represents information for the0-30 minute period of the base matrix 200 illustrated in FIG. 2A. Asimilar matrix may be generated for each remaining time periods for TBS,which are illustrated in FIG. 2A.

Consider the first demo D1 in the 0-30 minute period (FIG. 2A), for TBS,with the program attributes PA, and time attributes TA, collectivereferenced as D1_TBS[PA,TA], 232 a. The values for D1_TBS[PA,TA] areacquired from the demo matrix element 210 a, in FIG. 2A. For the firstdemo D1, a vector of the sum (Σ(PA,TA)) of program attributes and thetime attributes for the each of the owned competitor networks, which arein bucket 1 is determined. The vector of the sum (Σ(PA,TA)) isdetermined by adding the individual contribution for each ownedcompetitor network in bucket B1, which is found in the row for 0-30minute (first 30-minute) period of the base matrix 200 illustrated inFIG. 2A, for a period of, for example, 1 year. Since different shows maybe presented in the 0-30 minute period, the vector of the sum (Σ(PA,TA))for bucket B1 is weighted. The resulting weighted vector for the summedattributes of content for the networks in bucket B1, may be representedas [W(Σ(PA,TA))]D1,B1]. An example weighted vector for the summedattributes of content for the networks in bucket B1 may comprise 60%light content, 20% news, 10% sport, and 10% movies, and is representedby reference number 232 b-1.

Bucket B2 is handled in a similar manner as bucket B1. In this regard,the vector of the sum (Σ(PA,TA)) is determined by adding the individualcontribution for each owned competitor network in bucket B2, which isfound in the row for 0-30 minute (first 30-minute) period of the basematrix 200 illustrated in FIG. 2A, for a period of, for example, 1 year.Since different shows may be presented in the 0-30 minute period, thevector of the sum (Σ(PA,TA)) for bucket B2 is weighted. The resultingweighted vector for the summed attributes of content for the networks inbucket B1, may be represented as [W(Σ(PA,TA))]D1,B2]. An exampleweighted vector for the summed attributes of content for the networks inbucket B2 may comprise 50% heavy content, 30% news, and 20% sport, andis represented by reference number 232 c-1.

Bucket B3 is handled in a similar manner as buckets B1 and B2. In thisregard, the vector of the sum (Σ(PA,TA)) is determined by adding theindividual contribution for each owned competitor network in bucket B3,which is found in the row for 0-30 minute (first 30-minute) period ofthe base matrix 200 illustrated in FIG. 2A, for a period of, forexample, 1 year. Since different shows may be presented in the 0-30minute period, the vector of the sum (Σ(PA,TA)) for bucket B3 isweighted. The resulting weighted vector for the summed attributes ofcontent for the networks in bucket B3, may be represented as[W(Σ(PA,TA))]D1,B3]. An example weighted vector for the summedattributes of content for the networks in bucket B2 may comprise 30%light content, 30% news, 20% sport, and 20% movies, and is representedby reference number 232 d-1.

Bucket B4 is handled in a similar manner as buckets B1, B2, and B3. Inthis regard, the vector of the sum (Σ(PA,TA)) is determined by addingthe individual contribution for each owned competitor network in bucketB4, which is found in the row for 0-30 minute (first 30-minute) periodof the base matrix 200 illustrated in FIG. 2A, for a period of, forexample, 1 year. Since different shows may be presented in the 0-30minute period, the vector of the sum (Σ(PA,TA)) for bucket B4 isweighted. The resulting weighted vector for the summed attributes ofcontent for the networks in bucket B4, may be represented as[W(Σ(PA,TA))]D1,B3]. An example weighted vector for the summedattributes of content for the networks in bucket B4 may comprise 60%light content, 30% news, and 10% sport, and is represented by referencenumber 232 e-1.

Utilizing the variables generated through the operations described abovewith respect to FIG. 2A and FIG. 2B, and equation 1 and equation 2, themost common approach is to estimate the parameters of this statisticalmodel (the betas) utilizing maximum likelihood estimation techniques.

This may be repeated for all the demos in the 0-30 minute period, sothat each demo may have a corresponding β vector. Once all the demos forthe 0-30 minute period are completed, the entire process may be repeatedfor the second 30-minute period (30-60), and so on until allcorresponding values of β for all demos in the 48^(th) 30-minute periodare completed.

The framework illustrated in FIGS. 2A and 2B may be utilized forpredictive modeling to determine viewership for an upcoming quarter.Once the β vector for a particular network is determined, this may beutilized to forecast or predict the viewership for an upcoming quarterfor the particular network. In order to forecast the viewership for TBSfor the upcoming quarter, the program schedule for the TBS and theprogram schedule for the competing networks are needed. Assume thattoday is Apr. 5, 2015, which is roughly the start of the second quarter2Q15, and that the model illustrated in FIG. 2A and FIG. 2B includesdata for the prior four quarters, namely, 2Q14, 3Q14, 4Q14, and 1Q15.Furthermore, it is assumed that all ratings and viewership information,for example, Nielsen data, for all prior periods have been received andare included in the model illustrated in FIG. 2A and FIG. 2B.

While the program schedule for the TBS network for 2Q15 is known, theprogram schedule for the competitors may not be known. However, it maybe assumed that the mix of program for the upcoming quarter is the sameas it was for the same quarter in the prior year. Accordingly, anassumption is made that the mix of program for 2Q15 is approximately thesame as the mix of program in 2Q14, which may be determined from thebucketized networks as illustrated in FIG. 2B (e.g. 231 b-1, 232 c-1,232 d-1, 232 e-1). If it is known that there is a change to theprogramming in a particular bucket, then the program mix in the bucketmay be adjusted accordingly. This information may be utilized toforecast the viewership for every demo, and for every 30-minute periodin the upcoming quarter utilizing the values that were calculated for13. The entire process may be repeated for other networks in order toforecast viewership for those other networks for the upcoming quarter.Furthermore, the various embodiments of the disclosure are not limitedto forecasting the current quarter, Q215. Accordingly, the modelpresented in FIG. 2A and FIG. 2B may also to utilize to forecast orpredict viewership for 3Q15, for example.

Since consumer behavior is constantly changing, the β vector for aparticular network may be updated based on these changes in the consumerbehavior. Accordingly, for example, once all the data for a quarter hasbeen received, the β vector for each particular network may be updatedto reflect the changes in consumer behavior.

Although the various embodiments of the disclosure presented withrespect to the model in FIG. 2A and FIG. 2B are utilized to predict theviewership for an owned network, it should be recognized that thedisclosure is not limited in this regard. Accordingly, the model in FIG.2A and FIG. 2B may also be utilized to predict viewership for competitornetworks.

The digital age allows individual tracking of consumers bysellers/advertisers since the sellers know the viewers they areinteracting with and can more readily learn their habits andpreferences. Consumers and viewers are tracked based on onlinepreferences, tracking cookies, Internet browsing habits, and locationbased services (LBS) to name a few. Based on this knowledge and habitsof the consumers and viewers, data from a plurality of data analyticssources may be fused together to provide more granular results. In thisregard, analytical information provided for a particular demo may give abreakdown of one or more categories that may fall within that particulardemo. For example, within the demo for people age 25-54, P[25-54], atthe 39^(th) 30-minute period (7:30-8:00 pm), which has a viewership of1M, 500K of the 1M viewers are cereal buyers. Given such granularity, anadvertiser of cereal would be more interested in targeting the 500Kcereal buyers rather that the entire 1M viewers.

Although targeting may operate with similar constraints as advertisementscheduling, aspects of targeting may be geared towards targetingsegments of a certain population on a more granular level. For example,consider the target [auto intenders], which includes people who intentto by automobiles. The target [Auto intenders] may comprise, a firstlevel of sub-targets, for example, [Ford branded autos], [Toyota brandedautos], [Honda branded Autos], and so on. Each of the first level ofsub-targets may comprise a second level of sub-targets. For example,target [Ford branded autos] may comprise second level sub-targets [FordSedans], [Ford SUVs], [Ford Trucks], and target [Toyota branded autos]may comprise second level sub-targets [Toyota Sedans], [Toyota SUVs],[Toyota Trucks], [Toyota Cross Overs], and so on. The second levelsub-targets [Ford Trucks] may comprise third level sub-targets, forexample, [Ford light trucks], and [Ford heavy duty trucks]. This levelof granularity enables targeting to zero in on specific segments toprovide a certain percentage lift during placement of targeting spots.Additional sub-levels of targets may be employed without departing fromthe spirit and scope of the disclosure.

The framework illustrated in FIG. 2A, and FIG. 2B may be modified suchthat the demos D1, D2, D3, . . . , DN may be replaced by targets, and aβ vector may be determined for each of the targets and for each of thenetworks. Exemplary targets may comprise cereal buyers, yogurt buyers,credit card buyers, luxury car buyers, buyers of US made cars, andbuyers of foreign made cars. A matrix of the targets may be generatedand advertisement may be more efficiently scheduled based on howtargeting deals are sold.

FIG. 2C is a diagram that illustrates an exemplary target matrix, inaccordance with various exemplary embodiment of the disclosure.Referring to FIG. 2C, there is shown target matrix 235, which comprisesexemplary target matrix 240, 242, and 244, each having correspondingelements referenced as 240 a, 240 b, 240 c, . . . , 240(N−1), 240(N);242 a, 242 b, 242 c, . . . , 242(N−1), 242(N); and 244 a, 244 b, 244 c,. . . , 244(N−1), 244(N), respectively.

FIG. 2D is a diagram that illustrates a focus determination for theframework for target audience rating estimation based on the targetmatrix of FIG. 2C, in accordance with various exemplary embodiments ofthe disclosure. Referring to FIG. 2D, there is shown an exemplary focusmatrix 260 including a focus network, TBS, and the bucketized networksin buckets, B1, B2, B3, and B4. The focus matrix 230 representsinformation for the 0-30 minute period of the base matrix 200illustrated in FIG. 2C. A similar matrix may be generated for eachremaining time periods for TBS, which are illustrated in FIG. 2D.

Consider the first target T1 in the 0-30 minute period (FIG. 2C), forTBS, with the program attributes PA, and time attributes TA, collectivereferenced as T1_TBS[PA,TA], 262 a. The values for T1_TBS[PA,TA] areacquired from the demo matrix element 240 a, in FIG. 2C. For the firsttarget T1, a vector of the sum (Σ(PA,TA)) of program attributes and thetime attributes for the each of the owned competitor networks, which arein bucket 1 is determined. The vector of the sum (Σ(PA,TA)) isdetermined by adding the individual contribution for each ownedcompetitor network in bucket B1, which is found in the row for 0-30minute (first 30-minute) period of the base matrix 235 illustrated inFIG. 2C for a period of, for example, 1 year. Since different shows maybe presented in the 0-30 minute period, the vector of the sum (Σ(PA,TA))for bucket B1 is weighted. The resulting weighted vector for the summedattributes of content for the networks in bucket B1, may be representedas [W(Σ(PA,TA))]T1,B1]. An example weighted vector for the summedattributes of content for the networks in bucket B1 may comprise 30%light content, 20% news, 40% sport, and 10% movies, and is representedby reference number 262 b-1.

Bucket B2 is handled in a similar manner as bucket B1. In this regard,the vector of the sum (Σ(PA,TA)) is determined by adding the individualcontribution for each owned competitor network in bucket B2, which isfound in the row for 0-30 minute (first 30-minute) period of the basematrix 235 illustrated in FIG. 2C, for a period of, for example, 1 year.Since different shows may be presented in the 0-30 minute period, thevector of the sum (Σ(PA,TA)) for bucket B2 is weighted. The resultingweighted vector for the summed attributes of content for the networks inbucket B1, may be represented as [W(Σ(PA,TA))]T1,B2]. An exampleweighted vector for the summed attributes of content for the networks inbucket B2 may comprise 40% heavy content, 30% news, and 30% sport, andis represented by reference number 262 c-1.

Bucket B3 is handled in a similar manner as buckets B1 and B2. In thisregard, the vector of the sum (Σ(PA,TA)) is determined by adding theindividual contribution for each owned competitor network in bucket B3,which is found in the row for 0-30 minute (first 30-minute) period ofthe base matrix 235 illustrated in FIG. 2C, for a period of, forexample, 1 year. Since different shows may be presented in the 0-30minute period, the vector of the sum (Σ(PA,TA)) for bucket B3 isweighted. The resulting weighted vector for the summed attributes ofcontent for the networks in bucket B3, may be represented as[W(Σ(PA,TA))]T1,B3]. An example weighted vector for the summedattributes of content for the networks in bucket B2 may comprise 30%light content, 30% news, 5% sport, and 35% movies, and is represented byreference number 262 d-1.

Bucket B4 is handled in a similar manner as buckets B1, B2, and B3. Inthis regard, the vector of the sum (Σ(PA,TA)) is determined by addingthe individual contribution for each owned competitor network in bucketB4, which is found in the row for 0-30 minute (first 30-minute) periodof the base matrix 235 illustrated in FIG. 2C, for a period of, forexample, 1 year. Since different shows may be presented in the 0-30minute period, the vector of the sum (Σ(PA,TA)) for bucket B4 isweighted. The resulting weighted vector for the summed attributes ofcontent for the networks in bucket B4, may be represented as[W(Σ(PA,TA))]T1,B3]. An example weighted vector for the summedattributes of content for the networks in bucket B4 may comprise 40%light content, 40% news, and 20% sport, and is represented by referencenumber 262 e-1.

Utilizing the variables, which correspond to targets instead of demos,as generated through the operations described above with respect to FIG.2C and FIG. 2D, and equation 1 and equation 2, the most common approachis to estimate the parameters of this statistical model (the betas)utilizing maximum likelihood estimation techniques.

This may be repeated for all the targets in the 0-30 minute period, sothat each target may have a corresponding β vector. Once all the targetsfor the 0-30 minute period are completed, the entire process may berepeated for the second 30-minute period (30-60), and so on until allcorresponding values of β for all targets in the 48^(th) 30-minuteperiod are completed.

A composite matrix comprising demos and targets may be generated, basedon, for example, FIG. 2B, and FIG. 2D.

The framework for audience rating estimation may be utilized togenerate, for example, an estimate of the percentage audience that maybe engaged in a certain activity. For example, the framework foraudience rating estimation may generate a percentage average audienceand/or a percentage total audience. In another aspect of the disclosure,the framework for audience rating estimation may generate an estimate ofthe actual number of persons that may be engaged in a certain activity.In this regard, the framework for audience rating estimation maygenerate an actual average audience or an actual total audience that maybe engaged in a certain activity based on a generated forecast of theaverage total audience and a forecast of the total audience.

The framework for audience rating estimation may take into account thenesting effect within a program. The nesting effect refers to thevarious factors that may affect the program. Each program may have aspecific effect, and this effect may be referred to as the baselineeffect. Each program has a fixed audience that is expected. Based on thefactors that may affect the program, the framework for audience ratingestimation may add or subtract from the fixed audience. For example,every 30-minute period may have an effect on the program, andseasonality or special events may have an effect on the program.

FIG. 3 is a high-level diagram illustrating exemplary generation ofaudience rating estimates and a dataset of simulated spot schedules andcorresponding attributes based on demographics and targets across one ormore networks, in accordance with an exemplary embodiment of thedisclosure. Referring to FIG. 3, there are shown sources of projectedaudience data 302, library 304, audience rating estimation generator305, storage 310, television programming scheduling system 312, andprogram schedule data 314.

The sources of projected audience data 302 may comprise a plurality ofaudience data sources 302 a, . . . , 302 n. Each of the plurality ofaudience data sources 302 a, . . . , 302 n are operable to generateprojected audience data for one or more of the broadcast provider'snetworks. One or more of the audience data sources 302 a, . . . , 302 nmay be operable to communicate projected audience data to the library304. Nielsen may be an example of an audience data source.

The library 304 comprises a repository for storing data. In this regard,the library 304 may comprise demographics (demo) data 304 a, and targetdata 304 b.

The audience rating estimate generator 305 may generate audience ratingestimates for all selling title-weeks across one or more of the networksthat are operated by the broadcast provider. In this regard, at 306, theaudience rating estimates may be generated for all selling title-weeksacross one or more of the networks utilizing the program schedule data314 by (1) demographics based on the demographics data 304 a, or (2)target based on the target data 304 b. The program schedule data 314 maybe received from the television programming scheduling system 312. At308, the audience rating estimates for all selling title weeks acrossone or more networks by demographics, and by target, may be stored inthe storage 310. The audience rating estimate generator 305 may alsohandle generation of data that may be utilized to determine reach. Inthis regard, at 316, the audience rating estimate generator 305 maygenerate simulated spot schedules and corresponding attributes for thesimulated spot schedules. At 318, the audience rating estimate generator305 may store a dataset of the simulated spot schedules andcorresponding attributes. The dataset of the simulated spot schedulesand corresponding attributes may be stored, for example, in the storage310. The audience rating estimate generator 305 may be part of theaudience proposal creator 142 c.

The storage 310 may be, for example, any kind storage device such as adisc, solid state memory, or other storage system such as a networkattached storage system (NAS) or cloud-based storage system. Theaudience rating estimates for all selling title-weeks across one or morenetworks by demographics and by target may be stored in a databasewithin the storage 310. The dataset of the simulated spot schedules andcorresponding attributes may also be stored in a database within thestorage 310. It should be recognized by those skilled in the art thatalthough the library 304 is illustrated differently from the storage310, the library 304 may be integrated within the storage 310.

FIG. 4A is a flow chart illustrating exemplary operations for processinga commercial break schedule by an advertisement scheduler, in accordancewith an exemplary embodiment of the disclosure. Referring to FIG. 4A,there is shown a flow chart 400 that describes example operations 402through 412 for processing by the advertisement scheduler 112.

At 402, the advertisement scheduler 112 may receive a commercial breakschedule generated from a completed assignment of one or more spots fordeals for processing. At 404, the advertisement scheduler 112 maydetermine a current indexing for the deals. The current indexing mayrepresent the projected liability per pending spot and is determinedbased on what has already been aired, and what will be aired in thefuture. At 406, the advertisement scheduler 112 may determine a forecastof expected demo viewership associated with constraints and/ordemographics for the deals is determined. In some embodiments of thedisclosure, the advertisement scheduler 112 may determine a weightingvalue for adjusting the forecast. At 408, the advertisement scheduler112 may reshuffle the placement of the one or more spots based on theindexing and the forecast. In instances where the weighting value foradjusting the forecast is determined, the advertisement scheduler 112may be operable to reshuffle the placement of the one or more spotsbased on the indexing and the weighted forecast. The weighting factormay be generated based on historical analysis of demographics estimates,for every demographics, for every network including what is airing onthe networks of competitors, at the 30-minute interval, and for anextended period, for example, one or more months, or one or more years.The weighting factor may take on a range of values, each of which may beupdated. In instances where the estimates may be dependent on previousestimates, then a weighting factor may be utilized in combination withthe previous estimate to weigh the error of the previous estimate andmeasurement. The weighting factor may be: (i) a constant: it may be asystem parameter chosen based on data and model performance analysis,for example, prior to system roll out. This constant can be reassessed,if need, periodically e.g. yearly; (ii) time-varying: the weight changesat every time step depending on the estimates and observations variance;(iii) adaptive: the weight changes at every time step as a function ofthe estimates error variance. In this regard, the adaptive weight may beviewed as time varying as a function of error

At 410, the advertisement scheduler 112 may generate a finalizedschedule based on the shuffling. At 412, the advertisement scheduler 112may apply the finalized commercial break schedule to the log/bin 152, orcommunicate the finalized commercial break schedule to the spotscheduler solver 110 for application to the log/bin 152. Additionaldetails on advertisement scheduling are disclosed in U.S. applicationSer. No. 14/842,799, which was filed on Sep. 1, 2015.

FIG. 4B is a flow chart illustrating exemplary operations for providingspot scheduling with targeting, in accordance with an exemplaryembodiment of the disclosure. Referring to FIG. 4B, there are shown aflow chart 420 comprising exemplary operations 422 through 436. Theexemplary operations 420 through 436 may be performed by one or morecomponents and/or functions of the television management system 100, forexample, the targeting processor 113.

At 422, a spot scheduler job is initiated. At 424, it is determinedwhether the prior week log is in a state to run targeting. If at 424 itis determined that the prior week log is not in a state to runtargeting, then at 426, it is not a targeted job.

If at 424 it is determined that the prior week log is in a state to runtargeting, then at 428, it is determined whether there are targetedspots in the job. At 428, if it is determined that there are no targetedspots in the job, then 426, it is not a targeted job. If at 428 it isdetermined that there are targeted spots in the job, then at 430, alltarget estimates for all specified targets in the job are acquired. At432, the baseline target delivery for the selling title is calculated.At 434, a lift factor and/or an error factor are applied to the baselinetargeted delivery to set a benchmark. At 436, the job is solved withminimum constraints for targets set equal to a benchmark by order.Additional details on targeting are disclosed in U.S. application Ser.No. 14/842,808, which was filed on Sep. 1, 2015.

FIG. 4C is a flow chart illustrating modification of the lift goal, inaccordance with an exemplary embodiment of the disclosure. Referring toFIG. 4C, there are shown a flow chart 440 comprising exemplaryoperations 442 through 450. The exemplary operations 442 through 450 maybe executed by the targeting processor 113.

At 442, targeting is initiated. At 444, it is determined whether thelift goal is achievable. If at 444 it is determined that the lift goalis not achievable, then at 448, a new achievable lift goal isdetermined. At 450, proceed with targeting using the newly achievablelift goal. If at 444 it is determined that the lift goal is achievable,then at 446, proceed with targeting using the original lift goal. Theexemplary operations may end (done) subsequent to operations 446 and450.

FIG. 4D is a high-level flow chart illustrating exemplary audienceplacement for advertiser spots that have an audience guarantee, inaccordance with an exemplary embodiment of the disclosure. Referring toFIG. 4D, there are shown exemplary operations 462 through 474. Theexemplary operations 462 through 474 may be performed by one or more ofthe components and functions of the television management system 100.

At 462, the spot scheduler job is initiated. At 464, it is determinedwhether the job contains audience spots. At 464, if it is determinedthat the job contains audience spots, then at 466, it is determinedwhether the prior week log is in a state to run audience operation. At466, if it is determined that the prior week log is in a state thatallows running of the audience operation, then at 468, audience segmentestimates for all audience segment in job are acquired. At 470, spotsare placed based on audience guarantee. At 472, audience spots on thelog are flagged or otherwise marked after placement. The flagging ormarking of the audience spots on the log after placement serves as anotification that the spot was placed by the audience operation andcautions movement of the audience spot.

If at 466, it is determined that the prior week log is not in a statethat allows running of the audience operation, then at 474, the job isnot an audience operation job. If at 704, it is determined that job doesnot contain audience spots, then at 474, the job is not an audienceoperation job. The exemplary operations may end (done) after 474.

In accordance with various embodiments of the disclosure, only a portionof deals that are made may be designated as targeting deals, and not allthe orders in a targeting deal have to be targeting orders. Accordingly,some deals may have no targeting orders, while other deals may have oneor more targeting orders. Furthermore, it is desirable to identifyinventory that can provide a certain lift when placing one or moretargeting spots which could be provided certain percentage lift. Thelift refers to a certain percentage over the baseline target delivery.For example, only the top 10% of the inventory for cereal buyers thatcould be provided a certain percentage lift may be targeted during the39^(th) period. The placement for the spot may be modified so that thecereal advertisement is presented during the 39^(th) period whenviewership of cereal buyers is at a maximum. One or more administration(admin) parameters may be utilized to set a default lift, and thedefault lift may be modified using the admin parameters.

In accordance with various embodiments of the disclosure, advertisementscheduling, targeting, and/or audience operation may be integrated. Foradvertisement scheduling, it is desirable to maximize delivery forcandidate deals that are not pacing as expected in order to reduce theliability. For targeting, it is desirable to utilize the inventory thatcould provide a certain percentage lift when placing the targetingspots. Since there may be a high correlation between the placement ofthe targeting spots and the placement of the advertisement schedulingspots, the placement of the targeting spots should not consume all theavailable spots since this would not permit the placement of theadvertisement scheduling spots to reduce the liability. In order toaddress this issue, a baseline target delivery may be determined foreach target. The spots may then be placed to achieve a determined liftbased on the baseline target delivery. For example, target A has a meanor median target delivery of 100. In order to provide a 25% lift, thebaseline target delivery for target A would be 125 after placement. Thedetermined lift is carefully determined in order to preventover-delivery and unnecessary consumption of the airtime inventory,thereby limiting the number of inventory that may be available foradvertisement scheduling. The delivery over the desired lift of spotscannot be reclaimed. For example, if the lift needed were only 10%, thenproviding a lift of 25% would result in a 15% additional delivery whatis expected.

The average target delivery may be determined by summing all thedelivery for a particular target in a 30-minute period within a bucket,and dividing by the number of deliveries of the target that occur withinthe 30-minute period within that bucket.

The median target delivery may be computed for each audiencesegment/order on each given network/selling title/week combination. Thecomputation may be based on, for example, two main inputs, namely, (1)estimated audience segment delivery of all the half hours within thatweek that correspond to the parent selling title; and (2) the number ofspots in the corresponding order that are scheduled in that parentselling title/week.

Let L represent list of half hours that correspond to the parent sellingtitle/week

Let n=size of L (number of half hours); and

Let m represent number of spots in that parent selling title/week in thecorresponding order.

The median schedule delivery may be computed as follows:

-   1. Let L^(S) be L sorted in descending order of audience segment    delivered. L^(S)(i) refers to the i-th element of L^(S), i=1, 2, . .    . , n.-   2. Let

r = m mod n and $c = \left\lfloor \frac{m}{n} \right\rfloor$

-   -   a. If r and n have the same parity [i.e., either n is odd and r        is odd or n is even and r is even]

${{Median}\mspace{14mu}{Schedule}\mspace{14mu}{Delivery}} = {{c{\sum\limits_{i = 1}^{n}{L^{S}(i)}}} + {\sum\limits_{i = {(\frac{r - m + 2}{2})}}^{(\frac{r + m}{2})}{L^{S}(i)}}}$

-   -   b. Else [i.e., r and m have different parities].

${{Median}\mspace{14mu}{Schedule}\mspace{14mu}{Delivery}} = {{c{\sum\limits_{i = 1}^{n}{L^{S}(i)}}} + {\sum\limits_{i = {(\frac{r - m + 1}{2})}}^{(\frac{r + m - 1}{2})}{L^{S}(i)}}}$

The lift goal may also be dynamically modified or changed over timebased on actual accruals. In this regard, if the actual target is lowerthan the forecasted target, the lift goal may be increased in order toincrease the viewership. It should be recognized that other metrics maybe utilized without departing from the spirit and scope of the variousembodiments of the disclosure.

In accordance with various embodiments of the disclosure, targeting mayor may not be a guaranteed service and for targeting impressions thatare not delivered, extra spots may sometimes be provided ascompensation. In one exemplary embodiment of the disclosure, the demodelivery may be guaranteed, but the expected lift may not be guaranteed.In another exemplary embodiment of the disclosure, the demo delivery maybe guaranteed, and the expected lift may also be guaranteed. In anotherexemplary embodiment of the disclosure, the lift may be guaranteed, butthe demo delivery may not be guaranteed.

FIG. 5A is a high-level diagram illustrating exemplary audience proposalgeneration for a pending deal, in accordance with an exemplaryembodiment of the disclosure. Referring to FIG. 5A, there are shownoperations 502 through 516. Operation 504 may be referred to as a datacollection 504. The data collection 504 comprise operations 504 a, . . ., 504 d. The operations 502 through 516 may occur with the advertisementmanagement system 100, which is illustrated in FIG. 1A.

At 502, for a pending deal for an advertiser, identify the target.

During data collection 504, at 504 a, a target baseline is determinedfor cost per thousand (CPM) comparison. At planning 504 b, variousparameters for the deal for the advertiser may be established based, forexample, on the determined target CPM goal. For example, demographicsrates (demo rates) per selling title (ST) for the advertiser areestablished. The demo rates specify how much the advertiser should becharged per spot when buying a demo. In general, the rate that ischarged for a spot which is sold by audience should be greater than orequal to the rate that is charge for selling the same spot by demo. Aspot that is sold by audience may be referred to as an audience spot. At504 c, additional constraints for the pending deal for the advertisermay be established. The additional constraints may comprise constraintsthat may be imposed by the planning operation 504 b. At 504 d, demorates by selling title for every network may be generated based onhistorical data for the advertiser for the pending deal in accordancewith the general and/or additional constraints.

At 506, audience rating estimates are gathered based on the identifiedtarget for the deal for the advertiser. The gathering of the audiencerating estimates at 506 occurs concurrently with the data collection504. The audience rating estimates for the deal for the advertiser maybe gathered from the library 304, and/or the storage 310, which areillustrated in FIG. 3.

At 508, data is gathered from the dataset of simulated spot schedulesand corresponding attributes. At 510, a reach prediction framework isutilized to generate audience reach for new spot schedules. Thegathering of the data from the dataset of simulated spot schedules andcorresponding attributes 508, and the generation of the audience reachfor new spot schedules 510 occurs concurrently with the gathering of theaudience rating estimates at 506, and concurrently with the datacollection 504. The data from the dataset may be gathered from thelibrary 304, and/or the storage 310, which are illustrated in FIG. 3.The outputs from 506, 508, 510, and 504 are communicated to 512.Operations 508 and 510 are executed when reach data is required. Forexample, operations 508, and 510 may be executed for a reach deal(reach-only deal), and for a mixture deal, that latter of which has bothan audience component and a reach component. In accordance with anaspect of the disclosure, there may be a dependency of the reachprediction framework on the generation of the audience rating estimates.Although the audience rating estimates may serve as an input to thereach prediction framework there may be some back and forth interactionor communication between the reach prediction framework, thedistribution framework, and the framework for audience rating estimatesin order to achieve the audience goals to generate the properdistribution for the allocation.

At 512, a distribution framework is utilized to generate a distributionacross networks and selling title-weeks based on, for example, (1) reachestimates for reach-only deals, or (2) on audience rating estimates, andreach estimates for mixture deals. In accordance with an exemplaryembodiment of the disclosure, the audience processor 153 may be operableto perform scheduling of audience spots based on a distribution ofaudience spots that is generated by the audience proposal creator 142 cutilizing the distribution framework. The distribution framework mayalso utilize the available capacity (avails) per selling title togenerate the distribution. The available capacity or avails isdetermined by subtracting the amount of inventory that has been soldfrom the total inventory (total capacity). The available capacity oravails is determined for each selling title-week. In other words, theavails represent the air time that is available to allocate the spots.At 512, in instances where a deal has a reach component, then a reachprediction framework is utilized to generate an audience reach for newspot schedules.

At 514, the generated distribution with desired reach (reach-only deals)is received, or the desired reach and audience (mixture deals) arereceived. This distribution generation is handled by the proposal system142, and may be referred to as the audience proposal creation. At 514,in instances where a deal has a reach component, then generated audiencereach for the new schedules are received. At 514, for mixture deals, thedesired reach and audience are received.

At 516, a proposal is generated based on the received distribution forthe pending reach-only deal for the advertiser, or mixture deal for theadvertiser. For example, for a mixture deal which has an audiencecomponent and a reach component, then the proposal is generated based onthe received distribution for the pending deal for the advertiser andthe generated audience reach.

In accordance with one embodiment of the disclosure, the identificationof the target 502, and the data collection 504 may be handled by theproposal system 142. The generation of the audience rating estimates,and the generation of the simulated spot schedules may be handled by theaudience rating estimate generator 305, and the audience proposalcreator 142 c may gather or acquire the audience rating estimates fromthe storage 310 and/or the library 304. The generation of thedistribution by the distribution framework at 512 may be handled by theaudience proposal creator 142 c.

It should readily be understood that although a proposal is generated at516, this proposal may be an interim proposal and not a final proposal.In this regard, the proposal generated at 516 may be communicated to theplanning 504 b of the data collection 504 for review and acceptance. Ifthe planning 504 b and the advertiser accepts the proposal, the proposalbecomes a finalized proposal. If the planning 504 b and/or theadvertiser rejects the proposal, the additional constraints may be addedat 504 c, and one or more of the operations 504 d, 508, 510, 512, 514,and 516 may be repeated until the planning 504 b and the advertiseraccepts the generated proposal. In this regard, one or more iterationsof the operations 504 d, 508, 510, 512, 514, and 516 may occur togenerate a finalized proposal.

In accordance with one embodiment of the disclosure, there may or maynot be a guarantee on the demo portion of the deal. However, there maybe a guarantee on the audience portion of the deal. Similarly, there maybe a guarantee on the reach portion or component of a deal.

FIG. 5B is a diagram that illustrates example audience planning for apending deal for an advertiser, in accordance with an exemplaryembodiment of the disclosure. Referring to FIG. 5B, there is shownplanning operation 504 b. Planning operation 504 b may includeparameters 534 for the deal for the advertiser, a previous period (old)proposal 530, and a new proposal 532 using audience operation. It shouldbe noted that the operations in FIG. 5B are not applicable to a reachcomponent of a deal.

The parameters 534 for the deal for the advertiser comprise budgetparameters 534 a, target CPM reduction parameter 534 b, and demo CPMparameter 534 c. The parameters 534 are negotiated between theadvertiser and the broadcast provider. The parameters 534 may bereferred to as general constraints. Optional constraints may comprise,for example, maximum units per selling title-week, a limit on the totalnumber of units in the deal, maximum impression and/or unit percentages(%) by network or selling title, and limits on rate increases andwhether or not to increase the rates in the same proportion. The maximumunits per selling title-week represents the maximum number or units thatmay be allocated to a selling title-week. The number of units limitrepresents a limit or cap on the total number of units for theparticular budget. The impression and/or unit percentages (%) by networkor selling title represents the maximum percentage of impressions and/orunits per network or selling title that can be allocated. The optionalparameters may also be negotiated between the advertiser and thebroadcast provider and/or may be imposed as part of the planningoperation 504 b. For example, the general and/or optional parameters maybe imposed during the one or more iterations that may occur in order togenerate a finalized proposal. For example, the proposal system 142 maybe operable to impose the general and/or optional constraints.

The budget parameters 534 a represent the total amount that theadvertiser wants or desires to spend on the deal, and which is to bedistributed across all the units. The target CPM reduction parameter 534b represents the percentage by which the planning operation 504 b willreduce the CPM for the target. The demo CPM parameter 534 c represents acap or maximum percentage by which the planning operation 504 b willincrease the demo CPM.

The previous period proposal 530 represents the previous deal that wasmade by the advertiser. As illustrated, for the previous period proposal530 included a demo CPM of $100, and a target CPM of $180. It should berecognized by one skilled in the art that the invention is not limitedto the use of the single previous period proposal 530. Accordingly, aplurality of other previous period proposals or other information may beutilized. For example, the previous year proposal 530 could be usedalong with audience rating estimates for the current year to determinethe demo CPM and/or target CPM.

A goal of the new proposal 532 is to reduce the target CPM that was orwould have been paid in the previous period proposal 530. Accordingly,in the new proposal 532, a target CPM is calculated based on the targetCPM reduction parameter 534 b of 5%. Accordingly, in the new proposal532, the target CPM is $171, and this is designated as the goal for thetarget CPM. Additionally, in the new proposal 532, a new demo CPM cap of$105 is calculated based on the demo CPM parameter 534 c of 5%. Underthe new proposal 532, the target CPM will be decreased by 5% to $171without causing an increase of more than 5% in the demo CPM. In otherwords, the demo CPM is going to be capped at $105.

FIG. 6 is a diagram that illustrates an exemplary allocation ofsimulated spots based on selling title, maximum allocation to sellingtitle, and maximum total units, in accordance with an exemplaryembodiment of the disclosure. Referring to FIG. 6, there is shown a grid600, comprising selling title and possible allocation of units. In orderto simplify the illustration, the weeks for the selling title weeks arenot shown, and allocation occurs in bins of 2 units. For this example,the flight is Oct. 1, 2015 (10/01/2015) through Oct. 15, 2015(10/15/2015), the maximum (max) unit allocation to selling title (ST) is6, the allocation bins is 2, and the maximum (max.) schedule total unitsis 10.

The grid 602 comprises the unit allocations schedule 602, and apercentage (%) allocation schedule 604. The unit allocation schedule 602shows the possible allocation of units 606 across selling titles ST1,ST2, and ST3. The total units allocated is derived by summing the unitallocations across the selling titles ST1, ST2, and ST3. For example,for the first allocation in which ST1 has 2 units, ST2 has 0 units, andST3 has 0 units, the total units is 2 (2+0+0). For the second allocationin which ST1 has 0 units, ST2 has 2 units, and ST3 has 0 units, thetotal units is 2 (0+2+0). For the tenth allocation in which ST1 has 6units, ST2 has 0 units, and ST3 has 0 units, the total units is 6(6+0+0). For the eleventh allocation in which ST1 has 4 units, ST2 has 2units, and ST3 has 0 units, the total units is 6 (4+2+0).

The percentage allocation schedule 604 for a particular selling title isderived by dividing the total number of units for the particularallocation by the units allocated for the selling title for theparticular allocation. For example, for the first allocation, thepercentage allocation schedule for ST1 is 2/2 or 100%, the percentageallocation schedule for ST2 is 0/2 or 0%, the percentage allocationschedule for ST3 is 0/2 or 0%. For the second allocation, the percentageallocation schedule for ST1 is 0/2 or 0%, the percentage allocationschedule for ST2 is 2/2 or 100%, the percentage allocation schedule forST3 is 0/2 or 0%. For the tenth allocation, the percentage allocationschedule for ST1 is 6/6 or 100%, the percentage allocation schedule forST2 is 0/6 or 0%, the percentage allocation schedule for ST3 is 0/6 or0%. For the eleventh allocation, the percentage allocation schedule forST1 is 4/6 or 67%, the percentage allocation schedule for ST2 is 2/6 or33%, the percentage allocation schedule for ST3 is 0/6 or 0%.

The distribution of total units that is desired may be input to thereach prediction framework as illustrated in the percentage allocationschedule 604, and the reach prediction framework may translate thedesired distribution to the unit level allocation. In this regard, themodeling is being done on the number of units. The percentage allocationschedule 604 may be an input to the audience proposal creator 142 c.

The goal of the audience reach estimation is to generate estimates ofunduplicated audience given a desired unit distribution framework. Inorder to estimate reach, large-scale sampling of simulated orhypothetical spot schedules from historical data may be employed basedon important advertiser decision points or inputs. Exemplary decisionpoints or inputs may include, but are not limited to: (1) advertisingcampaign/schedule flight (length of time, and time of year), (2)schedule target or demographic gross audience rating, and scheduletarget network(s), and general placement (time of day, selling title).

In order to simplify the sampling process, it is assumed that commercialminute-level audience ratings are somewhat homogenous within half-hourbins of time. Accordingly, commercial minutes are assigned to half-hourbins. The set of possible half-hour assignments to place an advertisingunit for any of a plurality of broadcast provider networks j in a chosenquarter (Qtr) may be expressed as the following set:H _(j) ={h:h is a half-hour with commercial time on Network j}Hence, the half-hour assignment capacity of all of the broadcastprovider's networks (BPN) in any given time period may be denoted as theunion of network half hour assignments possibilities:

$C_{BPN} = {\bigcup\limits_{j = 1}^{k}H_{j}}$

Let A equal all possible subsets of half-hours to which an advertisingunit may be assigned, defined by the power set of the capacity of theplurality of broadcast provider's networks (BPN):A=

(C _(BPN))={X:X⊆C _(BPN) and X is not empty}A set of possible half hour assignments may be created for advertisingunits with n∈

:n>0, units, assigned to n distinct half hour bins in the following way:a _(n) ={X:X⊆C _(BPN) ,|X|=n, and X is not empty}

Since the cardinality of any of these sets of spot schedules is massive,(e.g. set A has cardinality 2^(|C) ^(BPN) ^(|)−1), in order to simplifythe estimation problem, constraints may be imposed or enforced on thesampling. For example, one constraint may allow that one unit from anyspot schedule will air in a half-hour on any given network.Notwithstanding, units are allowed to air concurrently on distinctnetworks. Another constraint that may be imposed or enforced to furtherlimit possible unit assignments, includes constraining the selection ofhalf hours for unit placement to some user-defined subset of time, orselling category on networks of interest.

In an illustrative embodiment of the disclosure, a random sampling ofsimulated spot schedule 1 for ST1 and WK1 combination for a particularQuarter will yield or give x1 discrete half-hour assignments, and arandom spot S1 may be selected within a half-hour assignment. Based onthis sampling for simulated spot schedule 1, a length of the simulatedspot schedule 1, which is referred to as the flight for simulated spotschedule 1 may be determined. In an exemplary embodiment of thedisclosure, the flight for simulated spot schedule 1 may be determinedby computing a difference between a start spot and an end spot of thesimulated spot schedule 1. For simulated spot schedule 1, the number ofspots and the allocations are already known. The sum of the totalimpressions for simulated spot schedule 1 is equivalent to the grossaudience within each random sample of simulated spot schedule 1.Similarly, a random sampling of simulated spot schedule 2 for ST1 andWK2 combination for a particular Quarter will yield or give x2 discretehalf-hour assignments, and a random spot S2 may be selected within ahalf-hour assignment. Based on this sampling for simulated spot schedule2, a flight for simulated spot schedule 2 may be determined. In anexemplary embodiment of the disclosure, the flight for simulated spotschedule 2 may be determined by computing a difference between a startspot and an end spot of the simulated spot schedule 2. For simulatedspot schedule 2, the number of spots and the allocations for the sellingtitle and weeks combinations are already known. The sum of the totalimpressions for simulated schedule 2 is equivalent to the gross audiencewithin each random sample of simulated spot schedule 2. A spotseparation parameter may be utilized to, for example, increasepredictive accuracy, and provide the capability to estimate an impact(+/−) of evenly distributed unit allocation on a schedule's unduplicatedaudience. For example, instead of having 1 spot in ST/W1 and 12 spots inST/WK13, the spots may be more evenly distributed across the ST/WK forthe Qtr. The spot separation may be determined by computing, forexample, an average time distance between spots.

In order to provide a robust dataset, the sampling of the simulated spotschedules includes a wide range of spot counts, flight ranges, andallocations. Based on the generated dataset, a planner may select aflight (e.g. 3 weeks), percentage allocations across a specified set ofselling titles in order to provide a specified reach. In accordance withvarious embodiments of the disclosure, reach may be determined for asingle network or for a plurality of networks.

An exemplary sampling operation that may be utilized to build a datasetof simulated spot schedules may be as follows:

Input Parameters:

-   -   w=schedule flight length, where w is in an integer>0 expressing        the number of weeks an ad schedule will be in flight    -   u=unit count, where        u∈{5, 10, 15, 25, . . . ,:u≤some upper bound of proposed        schedule unit counts}    -   d=unit distribution across networks and selling category or        title    -   t=schedule target quarter or year        It should be noted that the bins for the unit count, u, may        utilize other increments, for example, two (2), or three (3),        without departing from the spirit and scope of the various        embodiments of the disclosure.        Output:        For k=1 to K (the desired number of schedules to sample):    -   Randomly sample a flight start date of length w based on start        dates with an end date less than or equal to the end of period t    -   Randomly sample, without replacement, possible half hour        assignments for unit selection subject to constraints d    -   Randomly sample a possible commercial minutes, within each        sampled half hour assignment        -   Compute schedule statistics and extract advertisement (Ad)            schedule characteristics from each sampled schedule, e.g.:            -   Unduplicated viewers and panel weights (depending on                data source)            -   Ad schedule gross rating points for target or                demographic audience            -   Ad schedule unit count            -   Ad schedule length (in days)            -   Ad schedule spot separation            -   Day of week distribution            -   Time of day distribution            -   Network and selling title distribution        -   Append ad schedule summary and characteristics to list of            sample schedules which comprise the simulated or            hypothetical spot schedules dataset which is stored in the            library.

Predictions of audience reach for new spot schedules may be generatedvia a prediction model utilizing the dataset of sampled spot scheduledata.

An exact formulation of the prediction model may vary depending on, forexample, whether the desired outcome for prediction is unduplicatedreach percentage (or probability of exposure), or actual reachedaudience (persons (000)). The prediction model may take on the form of ageneralized linear model:E[R]=g ⁻¹(Xβ)Where:

-   -   R is the desired outcome describing reach.    -   g stands for a link function to the desired probability        distribution.    -   X is a model matrix composed of variables extracted from the        sampled data.    -   β is a vector of parameters to be estimated.

Since sampled spot schedules only take on unit counts u, schedules wherethe desired unit count is not u∉{5, 10, 15, 25, . . . }, which mayinclude schedules such as {4, 6, 7, . . . }, may be estimated through alinear approximation through the model specified above.

FIG. 7 is a diagram illustrating exemplary operations for a frameworkfor simulated spot schedule generation and sampling, and audience reachestimation, in accordance with an exemplary embodiment of thedisclosure. Referring to FIG. 7, there is shown an operation flow 700comprising exemplary operations 702 through 724. The exemplary steps 702through 724 may be executed by the audience proposal creator 142 c.

At 702, define the selling title structure for deal for advertiser. At704, generate all possible allocations of spots. The generation of thepossible allocations of spots may include, at 706, discretizeallocations so that the allocations map to an integer number of spots.At 708, generate schedule spot counts based on historical informationfor the possible allocations. The generation of the schedule spot countsmay include, at 710, a wide range of spot counts from the advertiser forthe quarter are utilized. At 712, generate a dictionary of half-hour (30mins) assignments within the selling title and weeks combinations basedon the generated schedule spot counts. At 714, within the half-hourassignments are spots.

At 716, randomly sample, within the half-hour (½) hour assignments,thousands of schedules within the half-hour assignments identifiedwithin the selling title and weeks combinations for all the allocations.The random sampling may include, at 718, random sampling within thehalf-hour assignments over a quarter (Qtr) to provide schedulecharacteristics, simulated statistics, and the corresponding audience,which when combined, will result in a simulated schedule.

At 720, determine attributes from the simulated spot schedules to beutilized to generate the reach. Exemplary attributes may comprise: grossaudience, flight, allocation (distribution), spot separation, timeattributes (dayparts, dates, etc), program attributes, and networks. At722, a dataset of the simulated spot schedules and the correspondingattributes may be stored in a database within a library. At 724, aprediction model may be utilized to generate audience reach for new spotschedules based on the dataset in library.

FIG. 8 is a diagram that illustrates a distribution framework that maybe utilized to generate distribution across networks and selling titleand weeks combination, in accordance with an exemplary embodiment of thedisclosure. Referring to FIG. 8, there is shown a grid 800 that may beutilized for generating a distribution across networks and sellingtitle-weeks combinations. The grid 800 illustrates a mapping of allselling titles for all available networks 802 to a planning horizon 808,which is represented in weeks.

The networks 804 are referenced as NET1, . . . , NETm, where m is aninteger that is greater than or equal to 1. The selling titles (ST) 806are referenced as ST1, . . . , STn, where n is an integer that isgreater than or equal to 1. All the selling titles for network 1 (NET1)are referenced as ST1[NET1], . . . , STn[NET1]. All the selling titlesfor network 2 (NET2) are referenced as ST1 [NET2], . . . , STn[NET2].All the selling titles for network m (NETm) are referenced as ST1[NETm], . . . , STn[NETm].

The planning horizon 808 represents the weeks for the duration of thedeal, and may be 1 quarter (Qtr) or 13 weeks, 2 Qtrs or 26 weeks, 3 Qtrsor 39 weeks, 4 Qtrs or 52 weeks, and so on. The weeks are referenced asW1, W2, . . . , Wx, where x is an integer greater than or equal to 1.

For network 1, selling title 1, week 1, may be represented asST1[NET1]W1, 812. For network 2, selling title n, week 2, may berepresented as STn[NET2]W2, 814. For network 2, selling title 2, week 7,may be represented as ST2[NET2]W7, 816. For network m, selling title n,week x, may be represented as STn[NETm]Wx, 818.

For each network, and for each selling title-week combination, there isa corresponding rate parameter, demographics (demo) impressions oraudience for each demo parameter, and target impressions or audience foreach target parameter, which are referenced as parameters 810. Theparameters 810 may be represented as a vector. Accordingly, parameters810 may be referred to as parameter matrix or a parameter vector. Therate parameter may be constant across all the weeks or may vary acrossone or more weeks. For example, the rate parameter may vary based onquarter or other criteria such as, for example, demand. The demoimpressions for each demo parameter is a vector that represents anestimate of the average demo impressions for each demo for each sellingtitle and week combination. The target impressions for each targetparameter is a vector that represents an estimate of the average targetimpressions or audience for each target for each selling title and weekcombination. The avails parameter represents the air time that isavailable to allocate the spots.

Other inputs to the distribution framework include the parameters 534for the deal, namely, the advertiser budget parameters 534 a, target CPMreduction parameter 534 b, and demographics CPM maximum percentageincrease parameter 534 c, which may be referred to as generalconstraints. Optional constraints that may be input to the distributionframework may comprise, for example, maximum units per sellingtitle-week, a limit on the total number of units, impression and/or unitpercentages (%) by network or selling title, and limits on rateincreases and whether or not to increase them in the same proportion.Some of the inputs to the distribution framework may be hard constraintsthat have to be satisfied. For example, the budget parameters 534,target CPM reduction parameter 534 b, demo CPM maximum percentageincrease parameter 534 c, avails parameter, maximum units per sellingtitle-week, number of units limit, and limits on rate increases may behard constraints. Due to differences in availability of units throughoutthe quarter, it may be necessary to skew the distribution for aspecified period of time, for example, a number of weeks. Accordingly,some embodiments of the disclosure may comprise a skewing factor thatmay be utilized to skew the distribution for a specified period of time.Additional inputs include the rates per selling title which will be usedas the floor (minimum) prices to determine the value of the unitsallocated to the deal. This means that the rates could be increased as apart of the distribution framework, but they cannot be decreased. Itshould be understood by one skilled in the art that although a ratedecrease may not be desirable, the distribution framework mayaccommodate a decrease in rate if needed.

The distribution framework utilizes the inputs to determine, for eachdeal, for each advertiser, a distribution or schedule of units for eachselling title-week combination as well as the final rates to be chargefor the units on a selling title. In this regard, the distributionframework is operable to determine how many units are to be assigned orallocated to each selling title and week combination for each deal. Inother words, the distribution framework determines how many units are tobe assigned or allocated across all the available selling titles, acrossone or more networks, and across all the weeks such that all constraintsare satisfied. For example, a distribution should never assign moreunits to a selling title-week than the number of units that areavailable for that selling title on that specific week. In addition todetermining the number of units that are to be assigned or allocatedacross all the available selling titles, across one or more of thenetworks, and across all the weeks, the distribution model utilizes anobjective function to determine how much will be actually charged perunit for each selling title and week combination. The objective functionmaximizes how much the rate effectiveness can be increased.

The product of the allocated units per selling title and weekcombination, and the demo impressions yields or gives the number of demoimpressions per selling title and week combination. The demo CPM is thendetermined by dividing the budget by the total number of demoimpressions across all selling title and week combinations. The productof the allocated units per selling title and week combination, and thetarget impressions yields or gives the number of target impressions perselling title and week combination. The target CPM is then determined bydividing the budget by the total number of target impressions across allthe selling title and weeks combinations.

The distribution model implicitly examines all possible combinations ofschedule allocations and rate increments and determines whichcombination of schedule allocations and rate increments will provide themaximum rate effectiveness increase while at the same time meeting thebudget, the target CPM, the demo CPM, and the constraints.

A deal that has a reach component and an audience or demographicscomponent may be referred to as a mixture deal or mixture. In otherwords, a mixture deal can either be a deal with a reach component and agross audience (targeted audience) component, or a deal with a reachcomponent and a gross demographic viewership component. A mixture dealis measured by both the total gross viewership (demographics and/oraudience), and the unduplicated viewership, that is, reach, according toduration (time) and/or frequency qualifications. A mixture deal mayprovide a guarantee on only a portion of the deal. For example, amixture deal may require 2 million impressions (audience component ofdeal), of which 1M impressions are unique (reach component of deal)based on a specified qualification, and the remaining 1M are notrequired to be unique.

For reach target which is higher than expected, given the grossaudience, the parameters for the dataset may be modified in order toprovide the reach target. For example, the flight, allocation, programs,hours of day, network, and/or daypart parameters may be modified inorder to provide the reach target. In this regard, the mixture may beprovided by placing constraints on the parameters of the reach function.In other words, the reach function from the dataset may be utilized tocreate allocations across selling title and weeks combinations in orderto provide a particular reach, and by placing constraints or bounds onthe parameters for the reach function, the resulting reach may bealtered.

In accordance with an exemplary embodiment of the disclosure, a highlevel description of a formulation and steps are provided to determinethe unit distribution and pricing for reach and audience mixture by theaudience proposal creator 142 c. The audience proposal creator 142 c maybe operable to utilize the following exemplary input parameters,decision variables, and formulation to determine the unit distributionand pricing for reach and audience mixture:

Consider the following input parameters:

-   -   R_(sw)=Rate for units of selling title s in week w (may be        constant across weeks).    -   Δ_(T)=Percentage reduction in target CPM.    -   C^(T)=Baseline for target CPM.    -   Δ_(D)=Maximum percentage increase in demo CPM.    -   C^(D)=Baseline for demo CPM.    -   B=Advertiser budget for an audience-only deal (i.e., with no        reach guarantees).    -   Â_(sw) ^(D)=Estimated demo impressions on selling title s in        week W.    -   Â_(sw) ^(T)=Estimated target impressions on selling title s in        week w.    -   I_(sw)=Number of available airtime units on selling title s in        week w.    -   R=Minimum reach to be attained.    -   ƒ(y)=Reach as a function of several variables that include        flight, allocation, gross audience, time attributes, program        attributes, etc.        Decision Variables:    -   ρ_(sw)=Increment in the rate for selling title s in week w.    -   x_(sw)=Number of units to be allocated on selling title s in        week w.        Let the incremental rate efficiency (IRE) of a unit distribution        and pricing be defined as the sum of the rate increments times        the number of units allocated across all selling title-weeks,        i.e., the incremental rate efficiency,        IRE=Σ_(s)Σ_(w)ρ_(sw) x _(sw).

Exemplary steps for determining the unit distribution and pricing maycomprise: Step 1. Solving the following formulation to compute ordetermine the unit distribution and pricing that maximizes theincremental rate efficiency subject to restrictions on demo CPM, targetCPM, advertiser's budget, number of units available per sellingtitle-week, as well as other additional constraints. No reachconstraints are considered on this step.

$\begin{matrix}\; & {{Formulation}\mspace{14mu} 1} \\{{{Maximize}\mspace{14mu}{IRE}} = {\Sigma_{s}\Sigma_{w}\rho_{sw}x_{sw}}} & \; \\{{Such}\mspace{14mu}{that}} & \; \\{\frac{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}}{\Sigma_{s}\Sigma_{w}A_{sw}^{D}x_{sw}} \leq {\left( {1 + \Delta_{D}} \right)C^{D}}} & {{Constraint}\mspace{14mu} 1} \\{\frac{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}}{\Sigma_{s}\Sigma_{w}A_{sw}^{T}x_{sw}} \leq \left( {1 - \Delta_{T}} \right)} & {{Constraint}\mspace{14mu} 2} \\{{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}} \leq B} & {{Constraint}\mspace{14mu} 3} \\{x_{sw} \leq I_{sw}} & {{Constraint}\mspace{14mu} 4} \\{\left\lbrack x_{sw} \right\rbrack \in P} & {{Constraint}\mspace{14mu} 5} \\{x_{sw} \in Z^{+}} & \; \\{\rho_{sw} \in R^{+}} & \;\end{matrix}$This formulation maximizes the rate efficiency resulting from the unitdistribution and pricing across all selling title-weeks. The firstconstraint (constraint 1) enforces that the unit distribution andpricing will result in a demo CPM which does not exceed the maximumpercentage increase with respect to the baseline demo CPM. The secondconstraint (constraint 2) enforces that the unit distribution andpricing will result in a target CPM which does not exceed the reducedpercentage with respect to the baseline target CPM. The third constraint(constraint 3) enforces that the unit pricing and distribution shouldnot exceed the advertiser's budget. The fourth constraint (constraint 4)establishes that the unit distribution is limited by the number of unitsthat are available for each selling title-week. The fifth constraint(constraint 5) indicates that this formulation can also accommodateother additional constraints that may appear depending on thenegotiations with the advertiser. Examples of these additionalconstraints may include, for example:

-   -   Maximum number of units (total, by network, by selling title, by        selling title-week)    -   Maximum percentage of total impressions (by network, by selling        title)    -   Equitable distribution across weeks or skewed distribution based        on relative avails across weeks.    -   Limits on the rate increments.    -   Rate increments proportional to original rates, target        impressions, etc.        Finally, variables x_(sw) are defined over the set of        nonnegative integers, whereas variables ρ_(sw) are defined over        the set of nonnegative real numbers.

It should be noted that no reach constraints are considered on thisstep, so this is an upper bound on how much the rates may be increasedif there were no reach requirements to honor.

Let the maximum incremental rate efficiency determined in this step bedenoted by IRE₀*.

Step 2. Solve the following mathematical programming formulation todetermine the unit distribution and pricing that maximizes theincremental rate efficiency subject to the same constraints from step 1,plus reach constraints that guarantee that the allocation will providethe desired minimum reach.

$\begin{matrix}{{{{Maximize}\mspace{14mu}{IRE}} = {\Sigma_{s}\Sigma_{w}\rho_{sw}x_{sw}}}{{Such}\mspace{14mu}{that}}{\frac{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}}{\Sigma_{s}\Sigma_{w}A_{sw}^{D}x_{sw}} \leq {\left( {1 + \Delta_{D}} \right)C^{D}}}{\frac{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}}{\Sigma_{s}\Sigma_{w}A_{sw}^{T}x_{sw}} \leq {\left( {1 - \Delta_{T}} \right)C^{T}}}{{\Sigma_{s}{\Sigma_{w}\left( {R_{sw} + \rho_{sw}} \right)}x_{sw}} \leq B}{{x_{sw} \leq {I_{sw}\left\lbrack x_{sw} \right\rbrack}} \in P}{{f(y)} \geq R}{x_{sw} \in Z^{+}}{\rho_{sw} \in R^{+}}} & {{Formulation}\mspace{14mu} 2}\end{matrix}$

The main difference between formulation 2 and formulation 1, which isutilized in step 1, is the inclusion of reach constraints in formulation2 to guarantee that the unit distribution meets the desired minimumreach.

Let the maximum incremental rate efficiency determined in this step bedenoted by IRE_(R)*.

Step 3. Compute ΔIRE=IRE₀*−IRE_(R)*. This differential is theopportunity cost of including reach constraints, i.e., it is theincremental rate efficiency that is forgone by including reachguarantees. This opportunity cost is the minimum additional pricing thatneeds to be charged to the resulting unit distribution, i.e., theminimum reach premium.Step 4. Distribute ΔIRE across the unit distribution found in step 2.Let δ_(sw) be the additional charge for a unit on selling title s inweek w, and let x_(sw)* be the optimal number of units to be allocatedon selling title s in week w from step 2. A solution to the followingexpression may be determined:

${\Delta\;{IRE}} = {\sum\limits_{s}{\sum\limits_{w}{\delta_{sw}x_{sw}^{*}}}}$Several solutions are possible depending on whether it is desirable tospread the additional charges proportionally to the older rates, thetarget units, the demo units, etc.

FIG. 9 is a conceptual diagram illustrating an example of a hardwareimplementation for a television advertisement management systememploying a processing system for generation of reach, mixture, andpricing utilizing a framework for audience rating estimation, inaccordance with an exemplary embodiment of the disclosure. Referring toFIG. 9, the hardware implementation 900 for the television advertisementmanagement system 100 of FIG. 1A employs a processing system 914 foraudience proposal creation and spot scheduling utilizing the frameworkfor audience rating estimation, as described herein. In some examples,the processing system 914 may comprise one or more hardware processors904, a spot scheduler solver component 110, an advertisement schedulercomponent 112, a targeting component 113, and an audience component 114.

In this example, the television advertisement management system 100employing the processing system 914 may be implemented with a busarchitecture, represented generally by the bus 902. The bus 902 mayinclude any number of interconnecting buses and bridges depending on thespecific implementation of the television advertisement managementsystem 100 and the overall design constraints. The bus 902 linkstogether various circuits including one or more processors, representedgenerally by the processor 904, computer-readable media, representedgenerally by the computer-readable medium 906, the spot schedulercomponent 110, the advertisement scheduler component 112, the targetingcomponent 113, and the audience component 114 (which correspond to thespot scheduler 110, the advertisement scheduler 112, the targetingprocessor 113, and the audience processor 114, respectively, which areillustrated in illustrated in FIG. 1A), which may be configured to carryout one or more methods or procedures described herein.

The bus interface 908 provides an interface between the bus 902 and atransceiver 916. The transceiver 916 provides a means for communicatingvia the network 120 with various other apparatus such as the advertiserorder generation systems 130 a, . . . , 130 n and the consumer devices132 a, . . . , 132 n (FIG. 1A). The user interface 918 (e.g., keypad,display, speaker, microphone, pointing) may also be provided to enable auser to interact with the television advertisement management system 100(FIG. 1A). In accordance with an aspect of the disclosure, the userinterface 918 may enable user interaction with the televisionadvertisement management system 100. For example, the user interface 918may be utilized to enter administration (admin) and/or configurationparameters.

The processor 904 may be operable to manage the bus 902 and generalprocessing, including the execution of software stored on thecomputer-readable medium 906. In accordance with an embodiment of thedisclosure, the processor 904 may be operable to control the operationof the television advertisement management system 100 (FIG. 1A), and maybe operable to coordinate operation amongst the components therein, aswell as with entities external to the television advertisementmanagement system 100. The software, when executed by the processor 904,causes the television advertisement management system 100 to perform thevarious functions and/or operations described infra for any particularapparatus. The computer-readable medium 906 may also be used for storingdata that is manipulated by the processor 904 when executing software.

In an aspect, processor 904, computer-readable medium 906, or acombination of both may be configured or otherwise specially programmedto perform the functionality of the spot scheduler component 110, theadvertisement scheduler component 112, the targeting component 113, theaudience component 114, (corresponding to the spot scheduler 110,advertisement scheduler 112, targeting processor 113, and audienceprocessor 114, respectively, which are illustrated in FIG. 1A), thetelevision management advertisement management system 100, or variousother components described herein. For example, the processor 904, thecomputer-readable medium 906, or a combination of both may be configuredor otherwise specially programmed to perform the functionality of thespot scheduler component 110, the advertisement scheduler component 112,the targeting component 113, the audience component 114, and/orcomponents thereof, etc. described herein (e.g., the operations 160 inFIG. 1C, the operations 300 in FIG. 3, the operations 400 in FIG. 4A,the operations 420 in FIG. 4B, the operations 440 in FIG. 4C, theoperations 460 in FIG. 4D, the operations in FIG. 5A-5B, etc.), and theoperations 800 in FIG. 8, and/or the like.)

In accordance with an exemplary embodiment of the disclosure, a hardwareadvertisement management system communicates with a plurality ofadvertiser order generation systems and electronically receives, via acommunication network, deals comprising orders from advertisers. Anaudience proposal creator 142 c in the hardware advertisement managementsystem may generate schedule spot counts for allocations of spots basedon a selling title and week combination structure for a pending deal foran advertiser. The audience proposal creator 142 c may generate aplurality of simulated spot schedules based on the schedule spot countsfor the advertiser for a quarter. The schedule spot counts may begenerated based on historical information for possible allocations ofthe spots. The audience proposal creator 142 c may be operable togenerate estimates of unduplicated audience for new spot schedules basedon corresponding attributes for each of the plurality of simulated spotschedules and generate a proposal for the deal for the advertiser basedon the estimates of unduplicated audience. The audience proposal creator142 c may generate the half-hour assignments within the sellingtitle-weeks based on the schedule spot counts, and may randomly samplethe half-hour assignments over the quarter to generate the plurality ofsimulated spot schedules. For each of the plurality of simulated spotschedules, each random schedule resulting from the random samplingwithin the half-hour assignments over the quarter yields or gives a spotand a corresponding audience. The audience proposal creator 142 c mayconstrain the sampling to allow one unit from any simulated spotschedule to air in a half-hour assignment on any given network. Theaudience proposal creator 142 c may also constrain the selection of thehalf-hour assignments for unit placement to a specified subset of time,and/or a selling category on networks of interest. The audience proposalcreator 142 c may be operable to compute or otherwise generate simulatedspot statistics for each of the plurality of simulated spot schedulesbased on the corresponding attributes. The simulated spot statistics maycomprise at least one of unduplicated viewers and panel weights, spotschedule gross rating points for target or demographic audience, spotschedule unit count, spot schedule length (in days), spot scheduleseparation, day of week distribution, time of day distribution, andnetwork and selling title distribution.

In accordance with another aspect of the disclosure, audience proposalcreator 142 c may determine a target cost per thousand (CPM) baselineand a demographics CPM baseline for the pending deal for the advertiserin which audience spots will be offered, and establish parameters forthe pending deal for the advertiser. Constraints to be imposed on thepending deal may be determined by the audience proposal creator 142 cbased on a target CPM reduction goal, a demographics CPM cap, andestablished parameters for the pending deal. The audience proposalcreator 142 c may generate rates by selling title for each selling titleof a plurality of selling titles, for each week of a plurality of weeksfor a duration of the pending deal, and for each network of a pluralityof networks, for the pending deal for the advertiser, based on theconstraints. Concurrent with the establishing of the parameters, thedetermining of the constraints, and the generating of the rates byselling title, the audience proposal creator 142 c may acquire targetaudience rating estimates based on the target CPM reduction goal, andthe demographics CPM cap for the plurality of networks. The audienceproposal creator 142 c may generate a distribution of the audience spotsacross the plurality of selling titles, the plurality of weeks, and theplurality of networks based at least in part on the target audiencerating estimates, reach estimates, a budget for the pending deal, thegenerated rates by selling title, and available inventory per sellingtitle-weeks, and may generate the proposal based on the distribution.

The audience proposal creator 142 c may schedule audience spots acrossone or more networks for selling title-weeks based on the generateddistribution. The parameters for the pending deal may comprise thebudget, the target CPM reduction goal, the demographics CPM cap, anddemographics rates per selling title to be charged per spot for thepending deal for the advertiser. The audience proposal creator 142 c maygenerate the distribution of the audience spots utilizing a distributionframework, and inputs to the distribution framework comprise the budget,the target CPM reduction goal, the demographics CPM cap, the estimatesof unduplicated audience, estimates of gross audience, and one or moreof a maximum number of units to be allocated per selling title-week, alimit on the total number of units in the pending deal, a limit on thenumber of impressions and/or units percentages by network or sellingtitle, a limit on rate increase, and an indication of whether toincrease the rates in the same proportion.

The concurrent operation of the audience proposal creator 142 c tohandle the establishing of the parameters, the determining of theconstraints, and the generating of the rates by selling title, and toacquire target audience rating estimates based on the target CPMbaseline, and the demographics CPM cap for the plurality of networks, aswell as concurrently gather data from dataset of simulated schedules andcorresponding attributes, and generate audience reach for new spotschedules enables the television advertisement management system 100 tooperate more efficiently since processing resources and memory may bemore efficiently utilized by the concurrent operations. The concurrentoperation also increases the operating speed (faster computation timewithout sacrificing accuracy) of the television advertisement managementsystem 100 when generating reach and the distribution of the audiencespots across the plurality of selling titles, the plurality of weeks,and the plurality of networks based at least in part on the targetaudience rating estimates, a budget for the pending deal, the generatedrates by selling title, and available inventory per selling title-weeks.The television advertisement management system 100 operates faster andmore efficiently to accommodate reach and the distributing of unitsacross selling titles in multiple networks at the same time orconcurrently, and can operate to process impressions from more targetedaudience segments which are smaller than broader demographics expressedin terms of age and gender. The concurrent operation and use of the datafor the framework for target audience rating estimation and demoaudience rating estimation, and for the reach prediction framework mayutilize less memory than would otherwise be required resulting in muchfaster processing time.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. Likewise, the term “embodiments ofthe invention” does not require that all embodiments of the inventioninclude the discussed feature, advantage or mode of operation.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and/or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprise afirst “circuit” when executing a first one or more lines of code and maycomprise a second “circuit” when executing a second one or more lines ofcode. As utilized herein, “and/or” means any one or more of the items inthe list joined by “and/or”. As an example, “x and/or y” means anyelement of the three-element set {(x), (y), (x, y)}. As another example,“x, y, and/or z” means any element of the seven-element set {(x), (y),(z), (x, y), (x, z), (y, z), (x, y, z)}. As utilized herein, the term“exemplary” means serving as a non-limiting example, instance, orillustration. As utilized herein, the terms “e.g.,” and “for example”set off lists of one or more non-limiting examples, instances, orillustrations. As utilized herein, circuitry is “operable” to perform afunction whenever the circuitry comprises the necessary hardware andcode (if any is necessary) to perform the function, regardless ofwhether performance of the function is disabled, or not enabled, by someuser-configurable setting.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of embodiments ofthe invention. As used herein, the singular forms “a”, “an” and “the”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. It will be further understood that theterms “comprises”, “comprising,”, “includes” and/or “including”, whenused herein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

Further, many embodiments are described in terms of sequences of actionsto be performed by, for example, elements of a computing device. It willbe recognized that various actions described herein can be performed byspecific circuits (e.g., application specific integrated circuits(ASICs)), by program instructions being executed by one or moreprocessors, or by a combination of both. Additionally, these sequence ofactions described herein can be considered to be embodied entirelywithin any non-transitory form of computer readable storage mediumhaving stored therein a corresponding set of computer instructions thatupon execution would cause an associated processor to perform thefunctionality described herein. Thus, the various aspects of thedisclosure may be embodied in a number of different forms, all of whichhave been contemplated to be within the scope of the claimed subjectmatter. In addition, for each of the embodiments described herein, thecorresponding form of any such embodiments may be described herein as,for example, “logic configured to” perform the described action.

Another embodiment of the disclosure may provide a non-transitorymachine and/or computer readable storage and/or media, having storedthereon, a machine code and/or a computer program having at least onecode section executable by a machine and/or a computer, thereby causingthe machine and/or computer to perform the operations and/or steps asdescribed herein for targeting and demographics scheduling utilizing theframework for audience rating estimation.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

Further, those of skill in the art will appreciate that the variousillustrative logical blocks, modules, circuits, algorithm, operations,and/or steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software,firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, operations, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present invention.

The methods, sequences and/or algorithms described in connection withthe embodiments disclosed herein may be embodied directly in firmware,hardware, in a software module executed by a processor, or in acombination thereof. A software module may reside in RAM memory, flashmemory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, aremovable disk, a CD-ROM, or any other form of storage medium known inthe art. An exemplary storage medium is coupled to the processor suchthat the processor can read information from, and write information to,the storage medium. In the alternative, the storage medium may beintegral to the processor.

While the present invention has been described with reference to certainembodiments, it will be noted understood by, for example, those skilledin the art that various changes and modification could be made andequivalents may be substituted without departing from the scope of thepresent invention as defined, for example, in the appended claims. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the present invention without departingfrom its scope. The functions, operations, steps and/or actions of themethod claims in accordance with the embodiments of the inventiondescribed herein need not be performed in any particular order.Furthermore, although elements of the invention may be described orclaimed in the singular, the plural is contemplated unless limitation tothe singular is explicitly stated. Therefore, it is intended that thepresent invention not be limited to the particular embodiment disclosed,but that the present invention will include all embodiments fallingwithin the scope of the appended claims.

What is claimed is:
 1. A method, comprising: in a hardware advertisementmanagement system configured to communicate with a plurality ofadvertiser order generation systems and electronically receive, via acommunication network, deals comprising orders from advertisers:generating schedule spot counts for allocations of spots based on aselling title and week combination structure for a pending deal for anadvertiser; generating a plurality of simulated spot schedules based atleast on the schedule spot counts for the advertiser for a quarter;generating estimates of unduplicated audience for new spot schedulesbased on corresponding attributes of each of the plurality of simulatedspot schedules; establishing parameters for the pending deal for theadvertiser; determining constraints to be imposed on the pending dealbased on a reduction in a target cost per thousand (CPM) goal, a cap ondemographics CPM, and the established parameters for the pending deal;acquiring audience rating estimates concurrently with the establishingof the parameters and the determining of the constraints, wherein theacquiring of the audience rating estimates is based on the reduction inthe target CPM goal and the cap on the demographics CPM for a pluralityof networks; generating a distribution of audience spots for the pendingdeal for the advertiser across at least one of a plurality of sellingtitles, a plurality of weeks, or the plurality of networks based on atleast one of the audience rating estimates, reach estimates, or a budgetfor the pending deal; generating a proposal based on the estimates ofunduplicated audience and the distribution; and scheduling the audiencespots across one or more of the plurality of networks for selling titleand weeks combinations based on the generated proposal.
 2. The methodaccording to claim 1, further comprising generating the schedule spotcounts based on historical information for possible allocations of thespots.
 3. The method according to claim 1, further comprising generatinghalf-hour assignments for sampling within the selling title and weekcombination structure based on the schedule spot counts, wherein each ofthe half-hour assignments is a period of time assigned to at least oneadvertising unit over a given time period.
 4. The method according toclaim 3, further comprising randomly sampling within the half-hourassignments over the quarter to generate the plurality of simulated spotschedules.
 5. The method according to claim 4, wherein for each of theplurality of simulated spot schedules, each random schedule resultingfrom the random sampling within the half-hour assignments over thequarter provides schedule characteristics, schedule statistics, and acorresponding audience.
 6. The method according to claim 4, furthercomprising constraining the random sampling to allow one unit from asimulated spot schedule of the plurality of simulated spot schedules toair in one of the half-hour assignments on a given network of theplurality of networks.
 7. The method according to claim 4, furthercomprising constraining selection of the half-hour assignments for unitplacement to at least one of a specified subset of time or a sellingcategory on networks of interest from the plurality of networks.
 8. Themethod according to claim 1, further comprising computing simulated spotstatistics for each of the plurality of simulated spot schedules basedon the corresponding attributes.
 9. The method according to claim 8,wherein the simulated spot statistics comprises at least one ofunduplicated viewers and panel weights, spot schedule gross ratingpoints for at least one of a target audience or a demographic audience,spot schedule unit count, spot schedule length, spot scheduleseparation, day of week distribution, time of day distribution, ornetwork and selling title distribution.
 10. The method according toclaim 1, comprising: determining a target CPM baseline and ademographics CPM baseline for the pending deal for the advertiser inwhich the audience spots are offered; generating, based on theconstraints, rates by selling title for each selling title of theplurality of selling titles, for each week of the plurality of weeks fora duration of the pending deal, for each network of the plurality ofnetworks, and for the pending deal for the advertiser; acquiring theaudience rating estimates concurrently with the generating of the ratesby selling title based on the reduction in the target CPM goal, and thecap on the demographics CPM for the plurality of networks; andgenerating the distribution of the audience spots across the pluralityof selling titles, the plurality of weeks, and the plurality of networksfurther based on the generated rates by selling title, and availableinventory per selling title and weeks combination.
 11. The methodaccording to claim 10, wherein the parameters for the pending dealcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, and demographics rates per selling title to becharged per spot for the pending deal for the advertiser.
 12. The methodaccording to claim 10, further comprising generating the distribution ofthe audience spots utilizing a distribution framework, wherein inputs tothe distribution framework comprise the budget, the reduction in thetarget CPM goal, the cap on the demographics CPM, estimates of grossaudience, and the estimates of unduplicated audience.
 13. The methodaccording to claim 12, wherein the inputs to the distribution frameworkfurther comprise one or more of a maximum number of units to beallocated per selling title and week combination, a limit on a totalnumber of units in the pending deal, a limit on a number of impressionsby one of network or the selling title, a limit on a number of unitspercentages by one of the network or the selling title, a limit on rateincrease, and an indication of whether to increase the rates in a sameproportion.
 14. The method according to claim 1, wherein thecorresponding attributes includes at least one of gross audience, spotseparation, allocation, time attributes, or program attributes.
 15. Asystem, comprising: at least one processor in a hardware advertisementmanagement system, the hardware advertisement management system isconfigured to communicate with a plurality of advertiser ordergeneration systems and electronically receive, via a communicationnetwork, deals comprising orders from advertisers, the at least oneprocessor configured to: generate schedule spot counts for allocationsof spots based on a selling title and week combination structure for apending deal for an advertiser; generate a plurality of simulated spotschedules based at least on the schedule spot counts for the advertiserfor a quarter; generate estimates of unduplicated audience for new spotschedules based on corresponding attributes of each of the plurality ofsimulated spot schedules; establish parameters for the pending deal forthe advertiser; determine constraints to be imposed on the pending dealbased on a reduction in a target cost per thousand (CPM) goal, a cap ondemographics CPM, and the established parameters for the pending deal;acquire audience rating estimates concurrently with the establishment ofthe parameters and the determination of the constraints, wherein theaudience rating estimates is acquired based on the reduction in thetarget CPM goal and the cap on the demographics CPM for a plurality ofnetworks; generate a distribution of audience spots for the pending dealfor the advertiser across at least one of a plurality of selling titles,a plurality of weeks, or the plurality of networks based on at least oneof the audience rating estimates, reach estimates, or a budget for thepending deal; generate a proposal based on the estimates of unduplicatedaudience and the distribution; and schedule the audience spots acrossone or more of the plurality of networks for selling title and weekscombinations based on the generated proposal.
 16. The system accordingto claim 15, wherein the at least one processor is further configured togenerate the schedule spot counts based on historical information forpossible allocations of the spots.
 17. The system according to claim 15,wherein the at least one processor is further configured to generatehalf-hour assignments within the selling title and week combinationstructure based on the schedule spot counts, wherein each of thehalf-hour assignments is a period of time assigned to at least oneadvertising unit over a given time period.
 18. The system according toclaim 17, wherein the at least one processor is further configured toexecute a random sampling operation within the half-hour assignmentsover the quarter to generate the plurality of simulated spot schedules.19. The system according to claim 18, wherein for each of the pluralityof simulated spot schedules, each random schedule generated based on therandom sampling operation within the half-hour assignments over thequarter provides schedule characteristics, schedule statistics, and acorresponding audience.
 20. The system according to claim 18, whereinthe at least one processor is further configured to constrain the randomsampling operation to allow one unit from a simulated spot schedule ofthe plurality of simulated spot schedules to air in one of the half-hourassignments on a given network of the plurality of networks.
 21. Thesystem according to claim 18, wherein at least one processor is furtherconfigured to constrain selection of the half-hour assignments for unitplacement to at least one of a specified subset of time or a sellingcategory on networks of interest from the plurality of networks.
 22. Thesystem according to claim 15, wherein the at least one processor isfurther configured to compute simulated spot statistics for each of theplurality of simulated spot schedules based on the correspondingattributes.
 23. The system according to claim 22, wherein the simulatedspot statistics comprises at least one of unduplicated viewers and panelweights, spot schedule gross rating points for at least one of a targetaudience or a demographic audience, spot schedule unit count, spotschedule length, spot schedule separation, day of week distribution,time of day distribution, or network and selling title distribution. 24.The system according to claim 15, wherein the at least one processor isfurther configured to: determine a target CPM baseline and ademographics CPM baseline for the pending deal for the advertiser inwhich the audience spots are offered; generate, based on theconstraints, rates by selling title for each selling title of theplurality of selling titles, for each week of the plurality of weeks fora duration of the pending deal, for each network of the plurality ofnetworks, and for the pending deal for the advertiser; acquire theaudience rating estimates concurrently with the generation of the ratesby selling title based on the reduction in the target CPM goal, and thecap on the demographics CPM for the plurality of networks; and generatethe distribution of the audience spots across the plurality of sellingtitles, the plurality of weeks, and the plurality of networks furtherbased on the generated rates by selling title, and available inventoryper selling title and weeks combination.
 25. The system according toclaim 24, wherein the parameters for the pending deal comprise thebudget, the reduction in target CPM goal, the cap on the demographicsCPM, and demographics rates per selling title to be charged per spot forthe pending deal for the advertiser.
 26. The system according to claim24, wherein the at least one processor is further configured to generatethe distribution of the audience spots based on a distributionframework, wherein inputs to the distribution framework comprise thebudget, the reduction in the target CPM goal, the cap on thedemographics CPM, estimates of gross audience, and the estimates ofunduplicated audience.
 27. The system according to claim 26, wherein theinputs to the distribution framework further comprise one or more of amaximum number of units to be allocated per selling title and weekcombination, a limit on a total number of units in the pending deal, alimit on a number of impressions by one of network or the selling title,a limit on a number of units percentages by one of the network or theselling title, a limit on rate increase, and an indication of whether toincrease the rates in a same proportion.
 28. A non-transitorycomputer-readable medium in a hardware advertisement management systemthat is configured to communicate with a plurality of advertiser ordergeneration systems and electronically receive, via a communicationnetwork, deals comprising orders from advertisers, the non-transitorycomputer-readable medium comprising computer-executable instructions,which when executed by a processor, causes the processor to executeoperations, the operations comprising: generating schedule spot countsfor allocations of spots based on a selling title and week combinationstructure for a pending deal for an advertiser; generating a pluralityof simulated spot schedules based at least on the schedule spot countsfor the advertiser for a quarter; generating estimates of unduplicatedaudience for new spot schedules based on corresponding attributes ofeach of the plurality of simulated spot schedules; establishingparameters for the pending deal for the advertiser; determiningconstraints to be imposed on the pending deal based on a reduction in atarget cost per thousand (CPM) goal, a cap on demographics CPM, and theestablished parameters for the pending deal; acquiring audience ratingestimates concurrently with the establishing of the parameters and thedetermining of the constraints, wherein the acquiring of the audiencerating estimates is based on the reduction in the target CPM goal andthe cap on the demographics CPM for a plurality of networks; generatinga distribution of audience spots for the pending deal for the advertiseracross at least one of a plurality of selling titles, a plurality ofweeks, or the plurality of networks based on at least one of theaudience rating estimates, reach estimates, or a budget for the pendingdeal; generating a proposal based on the estimates of unduplicatedaudience and the distribution; and scheduling the audience spots acrossone or more of the plurality of networks for selling title and weekscombinations based on the generated proposal.
 29. The non-transitorycomputer-readable medium according to claim 28, further comprisinggenerating the schedule spot counts based on historical information forpossible allocations of the spots.
 30. The non-transitorycomputer-readable medium according to claim 28, further comprisinggenerating half-hour assignments for sampling within the selling titleand week combination structure based on the schedule spot counts,wherein each of the half-hour assignments is a period of time assignedto at least one advertising unit over a given time period.
 31. Thenon-transitory computer-readable medium according to claim 30, furthercomprising randomly sampling within the half-hour assignments over thequarter to generate the plurality of simulated spot schedules.
 32. Thenon-transitory computer-readable medium according to claim 31, whereinfor each of the plurality of simulated spot schedules, each randomschedule resulting from the random sampling within the half-hourassignments over the quarter provides schedule characteristics, schedulestatistics, and a corresponding audience.
 33. The non-transitorycomputer-readable medium according to claim 31, further comprisingconstraining the random sampling to allow one unit from a simulated spotschedule of the plurality of simulated spot schedules to air in one ofthe half-hour assignments on a given network of the plurality ofnetworks.
 34. The non-transitory computer-readable medium according toclaim 31, further comprising constraining selection of the half-hourassignments for unit placement to at least one of a specified subset oftime or a selling category on networks of interest of the plurality ofnetworks.
 35. The non-transitory computer-readable medium according toclaim 28, further comprising computing simulated spot statistics foreach of the plurality of simulated spot schedules based on thecorresponding attributes.
 36. The non-transitory computer-readablemedium according to claim 35, wherein the simulated spot statisticscomprises at least one of unduplicated viewers and panel weights, spotschedule gross rating points for at least one of a target audience or ademographic audience, spot schedule unit count, spot schedule length,spot schedule separation, day of week distribution, time of daydistribution, or network and selling title distribution.
 37. Thenon-transitory computer-readable medium according to claim 28, furthercomprising: determining a target CPM baseline and a demographics CPMbaseline for the pending deal for the advertiser in which the audiencespots are offered; generating, based on the constraints, rates byselling title for each selling title of the plurality of selling titles,for each week of the plurality of weeks for a duration of the pendingdeal, for each network of the plurality of networks, and for the pendingdeal for the advertiser; acquiring the audience rating estimatesconcurrently with the generating of the rates by selling title based onthe reduction in the target CPM goal, and the cap on the demographicsCPM for the plurality of networks; and generating the distribution ofthe audience spots across the plurality of selling titles, the pluralityof weeks, and the plurality of networks further based on the generatedrates by selling title, and available inventory per selling title andweeks combination.
 38. The non-transitory computer-readable mediumaccording to claim 37, wherein the parameters for the pending dealcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, and demographics rates per selling title to becharged per spot for the pending deal for the advertiser.
 39. Thenon-transitory computer-readable medium according to claim 37, furthercomprising generating the distribution of the audience spots utilizing adistribution framework, wherein inputs to the distribution frameworkcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, estimates of gross audience, and the estimates ofunduplicated audience.
 40. The non-transitory computer-readable mediumaccording to claim 39, wherein the inputs to the distribution frameworkfurther comprise one or more of a maximum number of units to beallocated per selling title and week combination, a limit on a totalnumber of units in the pending deal, a limit on a number of impressionsby one of network or the selling title, a limit on a number of unitspercentages by one of the network or the selling title, a limit on rateincrease, and an indication of whether to increase the rates in a sameproportion.
 41. A method, comprising: in a hardware advertisementmanagement system configured to communicate with a plurality ofadvertiser order generation systems and electronically receive, via acommunication network, deals comprising orders from advertisers:generating schedule spot counts for allocations of spots based on aselling title and week combination structure for a pending deal for anadvertiser; generating a plurality of simulated spot schedules based atleast on the schedule spot counts for the advertiser for a quarter;generating estimates of unduplicated audience for new spot schedulesbased on corresponding attributes of each of the plurality of simulatedspot schedules; determining a target cost per thousand (CPM) baselineand a demographics CPM baseline for the pending deal for the advertiserin which audience spots are offered; establishing parameters for thepending deal for the advertiser; determining constraints to be imposedon the pending deal based on a reduction in a target CPM goal, a cap ondemographics CPM, and the established parameters for the pending deal;generating, based on the constraints, rates by selling title for eachselling title of a plurality of selling titles, for each week of aplurality of weeks for a duration of the pending deal, and for eachnetwork of a plurality of networks, for the pending deal for theadvertiser; concurrent with the establishing of the parameters, thedetermining of the constraints, and the generating of the rates byselling title, acquiring target audience rating estimates based on thereduction in the target CPM goal, and the cap on the demographics CPMfor the plurality of networks; generating a distribution of the audiencespots across the plurality of selling titles, the plurality of weeks,and the plurality of networks based at least in part on the targetaudience rating estimates, reach estimates, a budget for the pendingdeal, the generated rates by selling title, and available inventory perselling title and weeks combination; and generating a proposal based onthe estimates of unduplicated audience and based on the distribution.42. The method according to claim 41, further comprising generating theschedule spot counts based on historical information for possibleallocations of the spots.
 43. The method according to claim 41, furthercomprising generating half-hour assignments for sampling within theselling title and week combination structure based on the schedule spotcounts, wherein each of the half-hour assignments is a period of timeassigned to at least one advertising unit over a given time period. 44.The method according to claim 43, further comprising randomly samplingwithin the half-hour assignments over the quarter to generate theplurality of simulated spot schedules.
 45. The method according to claim44, wherein for each of the plurality of simulated spot schedules, eachrandom schedule resulting from the random sampling within the half-hourassignments over the quarter provides schedule characteristics, schedulestatistics, and a corresponding audience.
 46. The method according toclaim 44, further comprising constraining the random sampling to allowone unit from a simulated spot schedule of the plurality of simulatedspot schedules to air in one of the half-hour assignments on a givennetwork of the plurality of networks.
 47. The method according to claim44, further comprising constraining selection of the half-hourassignments for unit placement to at least one of a specified subset oftime or a selling category on networks of interest from the plurality ofnetworks.
 48. The method according to claim 41, further comprisingcomputing simulated spot statistics for each of the plurality ofsimulated spot schedules based on the corresponding attributes.
 49. Themethod according to claim 48, wherein the simulated spot statisticscomprises at least one of unduplicated viewers and panel weights, spotschedule gross rating points for at least one of a target audience or ademographic audience, spot schedule unit count, spot schedule length,spot schedule separation, day of week distribution, time of daydistribution, or network and selling title distribution.
 50. The methodaccording to claim 41, wherein the parameters for the pending dealcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, and demographics rates per selling title to becharged per spot for the pending deal for the advertiser.
 51. The methodaccording to claim 41, further comprising generating the distribution ofthe audience spots utilizing a distribution framework, wherein inputs tothe distribution framework comprise the budget, the reduction in thetarget CPM goal, the cap on the demographics CPM, estimates of grossaudience, and the estimates of unduplicated audience.
 52. The methodaccording to claim 51, wherein the inputs to the distribution frameworkfurther comprise one or more of a maximum number of units to beallocated per selling title and week combination, a limit on a totalnumber of units in the pending deal, a limit on a number of impressionspercentages by one of network or the selling title, a limit on a numberof units percentages by one of the network or the selling title, a limiton rate increase, and an indication of whether to increase the rates ina same proportion.
 53. The method according to claim 41, wherein thecorresponding attributes includes at least one of gross audience, spotseparation, allocation, time attributes, or program attributes.
 54. Asystem, comprising: at least one processor in a hardware advertisementmanagement system, the hardware advertisement management systemconfigured to communicate with a plurality of advertiser ordergeneration systems and electronically receive, via a communicationnetwork, deals comprising orders from advertisers, the at least oneprocessor configured to: generate schedule spot counts for allocationsof spots based on a selling title and week combination structure for apending deal for an advertiser; generate a plurality of simulated spotschedules based at least on the schedule spot counts for the advertiserfor a quarter; generate estimates of unduplicated audience for new spotschedules based on corresponding attributes of each of the plurality ofsimulated spot schedules; determine a target cost per thousand (CPM)baseline and a demographics CPM baseline for the pending deal for theadvertiser in which audience spots are offered; establish parameters forthe pending deal for the advertiser; determine constraints to be imposedon the pending deal based on a reduction in a target CPM goal, a cap ondemographics CPM, and the established parameters for the pending deal;generate, based on the constraints, rates by selling title for eachselling title of a plurality of selling titles, for each week of aplurality of weeks for a duration of the pending deal, and for eachnetwork of a plurality of networks, for the pending deal for theadvertiser; concurrent with the establishment of the parameters, thedetermination of the constraints, and the generation of the rates byselling title, acquire target audience rating estimates based on thereduction in the target CPM goal, and the cap on the demographics CPMfor the plurality of networks; generate a distribution of the audiencespots across the plurality of selling titles, the plurality of weeks,and the plurality of networks based at least in part on the targetaudience rating estimates, reach estimates, a budget for the pendingdeal, the generated rates by selling title, and available inventory perselling title and weeks combination; and generate a proposal based onthe estimates of unduplicated audience and based on the distribution.55. The system according to claim 54, wherein the at least one processoris further configured to generate the schedule spot counts based onhistorical information for possible allocations of the spots.
 56. Thesystem according to claim 54, wherein the at least one processor isfurther configured to generate half-hour assignments within the sellingtitle and week combination structure based on the schedule spot counts,wherein each of the half-hour assignments is a period of time assignedto at least one advertising unit over a given time period.
 57. Thesystem according to claim 56, wherein the at least one processor isfurther configured to execute a random sampling operation within thehalf-hour assignments over the quarter to generate the plurality ofsimulated spot schedules.
 58. The system according to claim 57, whereinfor each of the plurality of simulated spot schedules, each randomschedule generated based on the random sampling operation within thehalf-hour assignments over the quarter provides schedulecharacteristics, schedule statistics, and a corresponding audience. 59.The system according to claim 57, wherein the at least one processor isfurther configured to constrain the random sampling operation to allowone unit from a simulated spot schedule of the plurality of simulatedspot schedules to air in one of the half-hour assignments on a givennetwork of the plurality of networks.
 60. The system according to claim57, wherein at least one processor is further configured to constrainselection of the half-hour assignments for unit placement to at leastone of a specified subset of time or a selling category on networks ofinterest from the plurality of networks.
 61. The system according toclaim 24, wherein the at least one processor is further configured tocompute simulated spot statistics for each of the plurality of simulatedspot schedules based on the corresponding attributes.
 62. The systemaccording to claim 61, wherein the simulated spot statistics comprisesat least one of unduplicated viewers and panel weights, spot schedulegross rating points for at least one of a target audience or ademographic audience, spot schedule unit count, spot schedule length,spot schedule separation, day of week distribution, time of daydistribution, or network and selling title distribution.
 63. The systemaccording to claim 24, wherein the parameters for the pending dealcomprise the budget, the reduction in target CPM goal, the cap on thedemographics CPM, and demographics rates per selling title to be chargedper spot for the pending deal for the advertiser.
 64. The systemaccording to claim 24, wherein the at least one processor is furtherconfigured to generate the distribution of the audience spots based on adistribution framework, wherein inputs to the distribution frameworkcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, estimates of gross audience, and the estimates ofunduplicated audience.
 65. The system according to claim 64, wherein theinputs to the distribution framework further comprise one or more of amaximum number of units to be allocated per selling title and weekcombination, a limit on a total number of units in the pending deal, alimit on a number of impressions by one of network or the selling title,a limit on a number of units percentages by one of the network or theselling title, a limit on rate increase, and an indication of whether toincrease the rates in a same proportion.
 66. The system according toclaim 54, wherein the corresponding attributes includes at least one ofgross audience, spot separation, allocation, time attributes, or programattributes.
 67. A non-transitory computer-readable medium in a hardwareadvertisement management system that is configured to communicate with aplurality of advertiser order generation systems and electronicallyreceive, via a communication network, deals comprising orders fromadvertisers, the non-transitory computer-readable medium comprisingcomputer-executable instructions, which when executed by a processor,causes the processor to execute operations, the operations comprising:generating schedule spot counts for allocations of spots based on aselling title and week combination structure for a pending deal for anadvertiser; generating a plurality of simulated spot schedules based atleast on the schedule spot counts for the advertiser for a quarter;generating estimates of unduplicated audience for new spot schedulesbased on corresponding attributes of each of the plurality of simulatedspot schedules; determining a target cost per thousand (CPM) baselineand a demographics CPM baseline for the pending deal for the advertiserin which audience spots are offered; establishing parameters for thepending deal for the advertiser; determining constraints to be imposedon the pending deal based on a reduction in a target CPM goal, a cap ondemographics CPM, and the established parameters for the pending deal;generating, based on the constraints, rates by selling title for eachselling title of a plurality of selling titles, for each week of aplurality of weeks for a duration of the pending deal, and for eachnetwork of a plurality of networks, for the pending deal for theadvertiser; concurrent with the establishing of the parameters, thedetermining of the constraints, and the generating of the rates byselling title, acquiring target audience rating estimates based on thereduction in the target CPM goal, and the cap on the demographics CPMfor the plurality of networks; generating a distribution of the audiencespots across the plurality of selling titles, the plurality of weeks,and the plurality of networks based at least in part on the targetaudience rating estimates, reach estimates, a budget for the pendingdeal, the generated rates by selling title, and available inventory perselling title and weeks combination; and generating a proposal based onthe estimates of unduplicated audience and based on the distribution.68. The non-transitory computer-readable medium according to claim 67,further comprising generating the schedule spot counts based onhistorical information for possible allocations of the spots.
 69. Thenon-transitory computer-readable medium according to claim 67, furthercomprising generating half-hour assignments for sampling within theselling title and week combination structure based on the schedule spotcounts, wherein each of the half-hour assignments is a period of timeassigned to at least one advertising unit over a given time period. 70.The non-transitory computer-readable medium according to claim 69,further comprising randomly sampling within the half-hour assignmentsover the quarter to generate the plurality of simulated spot schedules.71. The non-transitory computer-readable medium according to claim 70,wherein for each of the plurality of simulated spot schedules, eachrandom schedule resulting from the random sampling within the half-hourassignments over the quarter provides schedule characteristics, schedulestatistics, and a corresponding audience.
 72. The non-transitorycomputer-readable medium according to claim 70, further comprisingconstraining the random sampling to allow one unit from a simulated spotschedule of the plurality of simulated spot schedules to air in one ofthe half-hour assignments on a given network of the plurality ofnetworks.
 73. The non-transitory computer-readable medium according toclaim 70, further comprising constraining selection of the half-hourassignments for unit placement to at least one of a specified subset oftime or a selling category on networks of interest of the plurality ofnetworks.
 74. The non-transitory computer-readable medium according toclaim 67, further comprising computing simulated spot statistics foreach of the plurality of simulated spot schedules based on thecorresponding attributes.
 75. The non-transitory computer-readablemedium according to claim 74, wherein the simulated spot statisticscomprises at least one of unduplicated viewers and panel weights, spotschedule gross rating points for at least one of a target audience or ademographic audience, spot schedule unit count, spot schedule length,spot schedule separation, day of week distribution, time of daydistribution, or network and selling title distribution.
 76. Thenon-transitory computer-readable medium according to claim 67, whereinthe parameters for the pending deal comprise the budget, the reductionin the target CPM goal, the cap on the demographics CPM, anddemographics rates per selling title to be charged per spot for thepending deal for the advertiser.
 77. The non-transitorycomputer-readable medium according to claim 67, further comprisinggenerating the distribution of the audience spots utilizing adistribution framework, wherein inputs to the distribution frameworkcomprise the budget, the reduction in the target CPM goal, the cap onthe demographics CPM, estimates of gross audience, and the estimates ofunduplicated audience.
 78. The non-transitory computer-readable mediumaccording to claim 77, wherein the inputs to the distribution frameworkfurther comprise one or more of a maximum number of units to beallocated per selling title and week combination, a limit on a totalnumber of units in the pending deal, a limit on a number of impressionsby one of network or the selling title, a limit on a number of unitspercentages by one of the network or the selling title, a limit on rateincrease, and an indication of whether to increase the rates in a sameproportion.
 79. The non-transitory computer-readable medium according toclaim 67, wherein the corresponding attributes includes at least one ofgross audience, spot separation, allocation, time attributes, or programattributes.